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Cascade Speculative Drafting for Even Faster LLM Inference | https://openreview.net/forum?id=lZY9u0ijP7 | https://openreview.net/forum?id=lZY9u0ijP7 | Ziyi Chen,Xiaocong Yang,Jiacheng Lin,Chenkai Sun,Kevin Chang,Jie Huang | NIPS 2024,Poster | Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then reviews this draft to align with its output, and any acceptance by the target model results in a reduction of the number of the target model runs, ultimately improving efficiency. However, the drafting process in speculative decoding includes slow autoregressive generation and allocates equal time to generating tokens, irrespective of their importance. These inefficiencies collectively contribute to the suboptimal performance of speculative decoding. To further improve LLM inference, we introduce Cascade Speculative Drafting (CS Drafting), a speculative execution algorithm that incorporates two types of cascades. The *Vertical Cascade* eliminates autoregressive generation from neural models, while the *Horizontal Cascade* optimizes time allocation in drafting for improved efficiency. Combining both cascades, CS Drafting achieves greater speedup compared to the baselines in our experiments, while preserving the same output distribution as the target model. Our code is publicly available at https://github.com/lfsszd/CS-Drafting. | https://openreview.net/pdf/7d6cf3bf7fac4f5e70a8ef98098a47f541169c34.pdf |
Quantum Deep Equilibrium Models | https://openreview.net/forum?id=CWhwKb0Q4k | https://openreview.net/forum?id=CWhwKb0Q4k | Philipp Schleich,Marta Skreta,Lasse Bjørn Kristensen,Rodrigo Vargas-Hernandez,Alan Aspuru-Guzik | NIPS 2024,Poster | The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth increases expressivity, but also results in a detrimental accumulation of errors. Furthermore, the number of parameters involved in the PQC significantly influences the performance through the necessary number of measurements to evaluate gradients, which scales linearly with the number of parameters.
Motivated by this, we look at deep equilibrium models (DEQs), which mimic an infinite-depth, weight-tied network using a fraction of the memory by employing a root solver to find the fixed points of the network. In this work, we present Quantum Deep Equilibrium Models (QDEQs): a training paradigm that learns parameters of a quantum machine learning model given by a PQC using DEQs. To our knowledge, no work has yet explored the application of DEQs to QML models. We apply QDEQs to find the parameters of a quantum circuit in two settings: the first involves classifying MNIST-4 digits with 4 qubits; the second extends it to 10 classes of MNIST, FashionMNIST and CIFAR. We find that QDEQ is not only competitive with comparable existing baseline models, but also achieves higher performance than a network with 5 times more layers. This demonstrates that the QDEQ paradigm can be used to develop significantly more shallow quantum circuits for a given task, something which is essential for the utility of near-term quantum computers.
Our code is available at \url{https://github.com/martaskrt/qdeq}. | https://openreview.net/pdf/ee5a435e0f6ba173f8dfa3a37a2e0d0fbba4d37d.pdf |
GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs | https://openreview.net/forum?id=hW5QWiCctl | https://openreview.net/forum?id=hW5QWiCctl | Zhao Zhang,Ziwei Zhao,Dong Wang,Liwei Wang | NIPS 2024,Poster | Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon. | https://openreview.net/pdf/1c697327e7b2306dcb3fa000f7f7712661179b29.pdf |
Rapid Plug-in Defenders | https://openreview.net/forum?id=UMPedMhKWm | https://openreview.net/forum?id=UMPedMhKWm | Kai Wu,Yujian Betterest Li,Jian Lou,Xiaoyu Zhang,Handing Wang,Jing Liu | NIPS 2024,Poster | In the realm of daily services, the deployment of deep neural networks underscores the paramount importance of their reliability. However, the vulnerability of these networks to adversarial attacks, primarily evasion-based, poses a concerning threat to their functionality. Common methods for enhancing robustness involve heavy adversarial training or leveraging learned knowledge from clean data, both necessitating substantial computational resources. This inherent time-intensive nature severely limits the agility of large foundational models to swiftly counter adversarial perturbations. To address this challenge, this paper focuses on the \textbf{Ra}pid \textbf{P}lug-\textbf{i}n \textbf{D}efender (\textbf{RaPiD}) problem, aiming to rapidly counter adversarial perturbations without altering the deployed model. Drawing inspiration from the generalization and the universal computation ability of pre-trained transformer models, we propose a novel method termed \textbf{CeTaD} (\textbf{C}onsidering Pr\textbf{e}-trained \textbf{T}ransformers \textbf{a}s \textbf{D}efenders) for RaPiD, optimized for efficient computation. \textbf{CeTaD} strategically fine-tunes the normalization layer parameters within the defender using a limited set of clean and adversarial examples. Our evaluation centers on assessing \textbf{CeTaD}'s effectiveness, transferability, and the impact of different components in scenarios involving one-shot adversarial examples. The proposed method is capable of rapidly adapting to various attacks and different application scenarios without altering the target model and clean training data. We also explore the influence of varying training data conditions on \textbf{CeTaD}'s performance. Notably, \textbf{CeTaD} exhibits adaptability across differentiable service models and proves the potential of continuous learning. | https://openreview.net/pdf/cc7da26977f10358e4e756a435a638d2ad7405d3.pdf |
Ordered Momentum for Asynchronous SGD | https://openreview.net/forum?id=U2Mx0hSRwA | https://openreview.net/forum?id=U2Mx0hSRwA | Chang-Wei Shi,Yi-Rui Yang,Wu-Jun Li | NIPS 2024,Poster | Distributed learning is essential for training large-scale deep models.
Asynchronous SGD (ASGD) and its variants are commonly used distributed learning methods, particularly in scenarios where the computing capabilities of workers in the cluster are heterogeneous.
Momentum has been acknowledged for its benefits in both optimization and generalization in deep model training. However, existing works have found that naively incorporating momentum into ASGD can impede the convergence.
In this paper, we propose a novel method called ordered momentum (OrMo) for ASGD. In OrMo, momentum is incorporated into ASGD by organizing the gradients in order based on their iteration indexes. We theoretically prove the convergence of OrMo with both constant and delay-adaptive learning rates for non-convex problems. To the best of our knowledge, this is the first work to establish the convergence analysis of ASGD with momentum without dependence on the maximum delay. Empirical results demonstrate that OrMo can achieve better convergence performance compared with ASGD and other asynchronous methods with momentum. | https://openreview.net/pdf/70e16903503ce6fa76e9df2a300c8f95295f2509.pdf |
Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization | https://openreview.net/forum?id=eAqcVZx30k | https://openreview.net/forum?id=eAqcVZx30k | Wei Liu,Zhiying Deng,Zhongyu Niu,Jun Wang,Haozhao Wang,YuanKai Zhang,Ruixuan Li | NIPS 2024,Poster | An important line of research in the field of explainability is to extract a small subset of crucial rationales from the full input. The most widely used criterion for rationale extraction is the maximum mutual information (MMI) criterion. However, in certain datasets, there are spurious features non-causally correlated with the label and also get high mutual information, complicating the loss landscape of MMI. Although some penalty-based methods have been developed to penalize the spurious features (e.g., invariance penalty, intervention penalty, etc) to help MMI work better, these are merely remedial measures.
In the optimization objectives of these methods, spurious features are still distinguished from plain noise, which hinders the discovery of causal rationales.
This paper aims to develop a new criterion that treats spurious features as plain noise, allowing the model to work on datasets rich in spurious features as if it were working on clean datasets, thereby making rationale extraction easier.
We theoretically observe that removing either plain noise or spurious features from the input does not alter the conditional distribution of the remaining components relative to the task label. However, significant changes in the conditional distribution occur only when causal features are eliminated.
Based on this discovery, the paper proposes a criterion for \textbf{M}aximizing the \textbf{R}emaining \textbf{D}iscrepancy (MRD). Experiments on six widely used datasets show that our MRD criterion improves rationale quality (measured by the overlap with human-annotated rationales) by up to $10.4\%$ as compared to several recent competitive MMI variants. Code: \url{https://github.com/jugechengzi/Rationalization-MRD}. | https://openreview.net/pdf/2081235c67059bc2acdc065ef0fcce259f7eb1b5.pdf |
Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems | https://openreview.net/forum?id=m5106RRLgx | https://openreview.net/forum?id=m5106RRLgx | Lingjiao Chen,Jared Quincy Davis,Boris Hanin,Peter Bailis,Ion Stoica,Matei Zaharia,James Zou | NIPS 2024,Poster | Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses. However, there is little understanding of how the number of LM calls -- e.g., when asking the LM to answer each question multiple times and taking a majority vote -- affects such a compound system's performance. In this paper, we initiate the study of scaling properties of compound inference systems. We analyze, theoretically and empirically, how the number of LM calls affects the performance of Vote and Filter-Vote, two of the simplest compound system designs, which aggregate LM responses via majority voting, optionally applying LM filters. We find, surprisingly, that across multiple language tasks, the performance of both Vote and Filter-Vote can first increase but then decrease as a function of the number of LM calls. Our theoretical results suggest that this non-monotonicity is due to the diversity of query difficulties within a task: more LM calls lead to higher performance on "easy" queries, but lower performance on "hard" queries, and non-monotone behavior can emerge when a task contains both types of queries. This insight then allows us to compute, from a small number of samples, the number of LM calls that maximizes system performance, and define an analytical scaling model for both systems. Experiments show that our scaling model can accurately predict the performance of Vote and Filter-Vote systems and thus find the optimal number of LM calls to make. | https://openreview.net/pdf/933389eba2d451a433d83e7d55975efe12b0a17b.pdf |
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient | https://openreview.net/forum?id=aBmiyi7iA7 | https://openreview.net/forum?id=aBmiyi7iA7 | Vu C. Dinh,Lam Si Tung Ho,Cuong V. Nguyen | NIPS 2024,Poster | We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of activation functions in the ReLU family, leapfrog HMC for networks with these activation functions has a large local error rate of $\Omega(\epsilon)$ rather than the classical error rate of $\mathcal{O}(\epsilon^3)$. This leads to a higher rejection rate of the proposals, making the method inefficient. We then verify our theoretical findings through empirical simulations as well as experiments on a real-world dataset that highlight the inefficiency of HMC inference on ReLU-based neural networks compared to analytical networks. | https://openreview.net/pdf/942c535cbf05e39d929d1238b3c761fba01fa6da.pdf |
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning | https://openreview.net/forum?id=4OJdZhcwBb | https://openreview.net/forum?id=4OJdZhcwBb | Jacob Adkins,Michael Bowling,Adam White | NIPS 2024,Poster | The performance of modern reinforcement learning algorithms critically relies
on tuning ever increasing numbers of hyperparameters. Often, small changes in
a hyperparameter can lead to drastic changes in performance, and different environments require very different hyperparameter settings to achieve state-of-the-art
performance reported in the literature. We currently lack a scalable and widely
accepted approach to characterizing these complex interactions. This work proposes a new empirical methodology for studying, comparing, and quantifying the
sensitivity of an algorithm’s performance to hyperparameter tuning for a given set
of environments. We then demonstrate the utility of this methodology by assessing
the hyperparameter sensitivity of several commonly used normalization variants of
PPO. The results suggest that several algorithmic performance improvements may,
in fact, be a result of an increased reliance on hyperparameter tuning. | https://openreview.net/pdf/8eff860c8489c171cf2a36abbbeddb2bfad51ac8.pdf |
TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation | https://openreview.net/forum?id=ZpVTRQVX5b | https://openreview.net/forum?id=ZpVTRQVX5b | Chenyang Le,Yao Qian,Dongmei Wang,Long Zhou,Shujie LIU,Xiaofei Wang,Midia Yousefi,Yanmin Qian,Jinyu Li,Michael Zeng | NIPS 2024,Poster | There is a rising interest and trend in research towards directly translating speech from one language to another, known as end-to-end speech-to-speech translation. However, most end-to-end models struggle to outperform cascade models, i.e., a pipeline framework by concatenating speech recognition, machine translation and text-to-speech models. The primary challenges stem from the inherent complexities involved in direct translation tasks and the scarcity of data. In this study, we introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion yet facilitates end-to-end inference through joint probability. Furthermore, we propose two separated encoders to preserve the speaker’s voice characteristics and isochrony from the source speech during the translation process, making it highly suitable for scenarios such as video dubbing. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model. | https://openreview.net/pdf/b8d1936c6491d6c912b49703bfa1f9d232db22ca.pdf |
Adaptive Labeling for Efficient Out-of-distribution Model Evaluation | https://openreview.net/forum?id=uuQQwrjMzb | https://openreview.net/forum?id=uuQQwrjMzb | Daksh Mittal,Yuanzhe Ma,Shalmali Joshi,Hongseok Namkoong | NIPS 2024,Poster | Datasets often suffer severe selection bias; clinical labels are only available on patients for whom doctors ordered medical exams. To assess model performance outside the support of available data, we present a computational framework for adaptive labeling, providing cost-efficient model evaluations under severe distribution shifts. We formulate the problem as a Markov Decision Process over states defined by posterior beliefs on model performance. Each batch of new labels incurs a “state transition” to sharper beliefs, and we choose batches to minimize uncertainty on model performance at the end of the label collection process. Instead of relying on high-variance REINFORCE policy gradient estimators that do not scale, our adaptive labeling policy is optimized using path-wise policy gradients computed by auto-differentiating through simulated roll-outs. Our framework is agnostic to different uncertainty quantification approaches and highlights the virtue of planning in adaptive labeling. On synthetic and real datasets, we empirically demonstrate even a one-step lookahead policy substantially outperforms active learning-inspired heuristics. | https://openreview.net/pdf/4eefea64ae0afe13d30727e1893f662df3e3b799.pdf |
NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping | https://openreview.net/forum?id=y6qhVtFG77 | https://openreview.net/forum?id=y6qhVtFG77 | Yamin Li,Ange Lou,Ziyuan Xu,SHENGCHAO ZHANG,Shiyu Wang,Dario J. Englot,Soheil Kolouri,Daniel Moyer,Roza G Bayrak,Catie Chang | NIPS 2024,Poster | Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modality synthesis and brain decoding, the use of deep neural networks has emerged as a promising solution for inferring whole-brain, high-resolution fMRI features directly from electroencephalography (EEG), a more widely accessible and portable neuroimaging modality. Nonetheless, the complex projection from neural activity to fMRI hemodynamic responses and the spatial ambiguity of EEG pose substantial challenges both in modeling and interpretability. Relatively few studies to date have developed approaches for EEG-fMRI translation, and although they have made significant strides, the inference of fMRI signals in a given study has been limited to a small set of brain areas and to a single condition (i.e., either resting-state or a specific task). The capability to predict fMRI signals in other brain areas, as well as to generalize across conditions, remain critical gaps in the field. To tackle these challenges, we introduce a novel and generalizable framework: NeuroBOLT, i.e., Neuro-to-BOLD Transformer, which leverages multi-dimensional representation learning from temporal, spatial, and spectral domains to translate raw EEG data to the corresponding fMRI activity signals across the brain. Our experiments demonstrate that NeuroBOLT effectively reconstructs unseen resting-state fMRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions, achieving state-of-the-art accuracy with the potential to generalize across varying conditions and sites, which significantly advances the integration of these two modalities. | https://openreview.net/pdf/7637d729304c503ef5c555a139062365ae9005dc.pdf |
Bayesian Adaptive Calibration and Optimal Design | https://openreview.net/forum?id=m906PS5G9x | https://openreview.net/forum?id=m906PS5G9x | Rafael Oliveira,Dino Sejdinovic,David Howard,Edwin V. Bonilla | NIPS 2024,Poster | The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simulations and then calibrated against real observations. Current machine learning approaches, however, mostly rely on rerunning simulations over a fixed set of designs available in the observed data, potentially neglecting informative correlations across the design space and requiring a large amount of simulations. Instead, we consider the calibration process from the perspective of Bayesian adaptive experimental design and propose a data-efficient algorithm to run maximally informative simulations within a batch-sequential process. At each round, the algorithm jointly estimates the parameters posterior distribution and optimal designs by maximising a variational lower bound of the expected information gain. The simulator is modelled as a sample from a Gaussian process, which allows us to correlate simulations and real data with the unknown calibration parameters. We show the benefits of our method when compared to related approaches across synthetic and real-data problems. | https://openreview.net/pdf/1a3b58749c0c68a59d59059df64282b1a3adc0a4.pdf |
FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models | https://openreview.net/forum?id=1SmXUGzrH8 | https://openreview.net/forum?id=1SmXUGzrH8 | Gong Zhang,Kihyuk Sohn,Meera Hahn,Humphrey Shi,Irfan Essa | NIPS 2024,Poster | Few-shot fine-tuning of text-to-image (T2I) generation models enables people to create unique images in their own style using natural languages without requiring extensive prompt engineering. However, fine-tuning with only a handful, as little as one, of image-text paired data prevents fine-grained control of style attributes at generation. In this paper, we present FineStyle, a few-shot fine-tuning method that allows enhanced controllability for style personalized text-to-image generation. To overcome the lack of training data for fine-tuning, we propose a novel concept-oriented data scaling that amplifies the number of image-text pair, each of which focuses on different concepts (e.g., objects) in the style reference image. We also identify the benefit of parameter-efficient adapter tuning of key and value kernels of cross-attention layers. Extensive experiments show the effectiveness of FineStyle at following fine-grained text prompts and delivering visual quality faithful to the specified style, measured by CLIP scores and human raters. | https://openreview.net/pdf/75bf1ad3580ab645399dbf37996275aa30130566.pdf |
Linking In-context Learning in Transformers to Human Episodic Memory | https://openreview.net/forum?id=AYDBFxNon4 | https://openreview.net/forum?id=AYDBFxNon4 | Li Ji-An,Corey Yishan Zhou,Marcus K. Benna,Marcelo G Mattar | NIPS 2024,Poster | Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence. While many artificial intelligence models have a neuroscience counterpart, such connections are largely missing in Transformer models and the self-attention mechanism. Here, we examine the relationship between interacting attention heads and human episodic memory. We focus on induction heads, which contribute to in-context learning in Transformer-based large language models (LLMs). We demonstrate that induction heads are behaviorally, functionally, and mechanistically similar to the contextual maintenance and retrieval (CMR) model of human episodic memory. Our analyses of LLMs pre-trained on extensive text data show that CMR-like heads often emerge in the intermediate and late layers, qualitatively mirroring human memory biases. The ablation of CMR-like heads suggests their causal role in in-context learning. Our findings uncover a parallel between the computational mechanisms of LLMs and human memory, offering valuable insights into both research fields. | https://openreview.net/pdf/0bd34b9f6d0eaba66fd4ba873e8e84a2bdd91e14.pdf |
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment | https://openreview.net/forum?id=G0yxFmP87g | https://openreview.net/forum?id=G0yxFmP87g | Yonggan Fu,Zhongzhi Yu,Junwei Li,Jiayi Qian,Yongan Zhang,Xiangchi Yuan,Dachuan Shi,Roman Yakunin,Yingyan Celine Lin | NIPS 2024,Poster | Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the rapid evolution of computational platforms, which feature diverse resource constraints and deployment flows. These varying requirements necessitate LLMs that can adapt their structures (depth and width) for optimal efficiency across different platforms and application specifications. To address this critical gap, we propose AmoebaLLM, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning. In this way, AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications. Specifically, AmoebaLLM integrates three innovative components: (1) a knowledge-preserving subnet selection strategy that features a dynamic-programming approach for depth shrinking and an importance-driven method for width shrinking; (2) a shape-aware mixture of LoRAs to mitigate gradient conflicts among subnets during fine-tuning; and (3) an in-place distillation scheme with loss-magnitude balancing as the fine-tuning objective. Extensive experiments validate that AmoebaLLM not only sets new standards in LLM adaptability but also successfully delivers subnets that achieve state-of-the-art trade-offs between accuracy and efficiency. | https://openreview.net/pdf/6cccf970913f515d37d602734d01d0c947705492.pdf |
N-agent Ad Hoc Teamwork | https://openreview.net/forum?id=q7TxGUWlhD | https://openreview.net/forum?id=q7TxGUWlhD | Caroline Wang,Arrasy Rahman,Ishan Durugkar,Elad Liebman,Peter Stone | NIPS 2024,Poster | Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls *all* agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a *single* agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars with the same learning algorithm, yet once on the road, these cars must cooperate with cars from another company. Towards expanding the class of scenarios that cooperative learning methods may optimally address, we introduce $N$*-agent ad hoc teamwork* (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates. This paper formalizes the problem, and proposes the *Policy Optimization with Agent Modelling* (POAM) algorithm. POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors. Empirical evaluation on tasks from the multi-agent particle environment and StarCraft II shows that POAM improves cooperative task returns compared to baseline approaches, and enables out-of-distribution generalization to unseen teammates. | https://openreview.net/pdf/b2a493d4f38a4116108b0ba02a974d3b686c5421.pdf |
Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling | https://openreview.net/forum?id=BNnZwbZGpm | https://openreview.net/forum?id=BNnZwbZGpm | Junyi Li,Heng Huang | NIPS 2024,Poster | Bilevel Optimization has experienced significant advancements recently with the introduction of new efficient algorithms. Mirroring the success in single-level optimization, stochastic gradient-based algorithms are widely used in bilevel optimization. However, a common limitation in these algorithms is the presumption of independent sampling, which can lead to increased computational costs due to the unique hyper-gradient structure in bilevel problems. To address this challenge, we study the example-selection strategy for bilevel optimization in this work. More specifically, we introduce a without-replacement sampling based algorithm which achieves a faster convergence rate compared to its counterparts that rely on independent sampling. Beyond the standard bilevel optimization formulation, we extend our discussion to conditional bilevel optimization and also two special cases: minimax and compositional optimization. Finally, we validate our algorithms over both synthetic and real-world applications. Numerical results clearly showcase the superiority of our algorithms. | https://openreview.net/pdf/02a1d8edd1255179e52012fdd5a11e5b2a4e5acc.pdf |
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning | https://openreview.net/forum?id=QXkFC7D6p4 | https://openreview.net/forum?id=QXkFC7D6p4 | Evelyn Ma,Chao Pan,S. Rasoul Etesami,Han Zhao,Olgica Milenkovic | NIPS 2024,Poster | The performance of Transfer Learning (TL) significantly depends on effective pretraining, which not only requires extensive amounts of data but also substantial computational resources. As a result, in practice, it is challenging to successfully perform TL at the level of individual model developers. Federated Learning (FL) addresses these challenges by enabling collaboration among individual clients through an indirect expansion of the available dataset, distribution of the computation burden across different entities, and privacy-preserving communication mechanisms. Despite several attempts to devise effective transferable FL approaches, several important issues remain unsolved. First, existing methods in this setting primarily focus on optimizing transferability within their local client domains, thereby ignoring transferability over the global learning domain. Second, most approaches focus on analyzing indirect transferability metrics, which does not allow for accurate assessment of the final target loss and extent of transferability. To address these issues, we introduce two important FL features into the model. The first boosts transferability via an exchange protocol between the clients and the server that includes information about cross-client Jacobian (gradient) norms. The second feature promotes an increase of the average of the Jacobians of the clients at the server side, which is subsequently used as a local regularizer that reduces the cross-client Jacobian variance. A rigorous analysis of our transferable federated algorithm, termed FedGTST (Federated Global Transferability via Statistics Tuning), reveals that increasing the averaged Jacobian norm across clients and reducing its variance ensures tight control of the target loss. This insight leads to the first known upper bound on the target loss of transferable federated learning in terms of the source loss and source-target domain discrepancy. Extensive experimental results on datasets including MNIST → MNIST-M and CIFAR10 → SVHN suggest that FedGTST significantly outperforms other relevant baselines, such as FedSR. For example, on the second source-target dataset pair, we improve the accuracy of FedSR by 9.8% and that of FedIIR by 7.6% when the backbone used is LeNet. | https://openreview.net/pdf/5654ab819d08c8951c309ec6e440949b8155196b.pdf |
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning | https://openreview.net/forum?id=PnlCHQrM69 | https://openreview.net/forum?id=PnlCHQrM69 | Yangruibo Ding,Jinjun Peng,Marcus J. Min,Gail Kaiser,Junfeng Yang,Baishakhi Ray | NIPS 2024,Poster | Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data and the need for semantic understanding for complex tasks like debugging and program repair. We introduce a novel strategy, _monologue reasoning_, to train Code LLMs to reason comprehensive semantics, encompassing high-level functional descriptions, local execution effects of individual statements, and overall input/output behavior, thereby linking static code text with dynamic execution states.
We begin by collecting PyX, a clean Python corpus of fully executable code samples with functional descriptions and test cases.
We propose training Code LLMs not only to write code but also to understand code semantics by reasoning about key properties, constraints, and execution behaviors using natural language, mimicking human verbal debugging, i.e., rubber-duck debugging. This approach led to the development of SemCoder, a Code LLM with only 6.7B parameters, which shows competitive performance with GPT-3.5-turbo on code generation and execution reasoning tasks. SemCoder achieves 79.3% on HumanEval (GPT-3.5-turbo: 76.8%), 63.6% on CRUXEval-I (GPT-3.5-turbo: 50.3%), and 63.9% on CRUXEval-O (GPT-3.5-turbo: 59.0%). We also study the effectiveness of SemCoder's monologue-style execution reasoning compared to concrete scratchpad reasoning, showing that our approach integrates semantics from multiple dimensions more smoothly. Finally, we demonstrate the potential of applying learned semantics to improve Code LLMs' debugging and self-refining capabilities. Our data, code, and models are available at: https://github.com/ARiSE-Lab/SemCoder. | https://openreview.net/pdf/1e07d8e51eb5f904f2122b7a56ed6151e47c5cc0.pdf |
On $f$-Divergence Principled Domain Adaptation: An Improved Framework | https://openreview.net/forum?id=xSU27DgWEr | https://openreview.net/forum?id=xSU27DgWEr | Ziqiao Wang,Yongyi Mao | NIPS 2024,Poster | Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning. In this work, we improve the theoretical foundations of UDA proposed in Acuna et al. (2021) by refining their $f$-divergence-based discrepancy and additionally introducing a new measure, $f$-domain discrepancy ($f$-DD). By removing the absolute value function and incorporating a scaling parameter, $f$-DD obtains novel target error and sample complexity bounds, allowing us to recover previous KL-based results and bridging the gap between algorithms and theory presented in Acuna et al. (2021). Using a localization technique, we also develop a fast-rate generalization bound. Empirical results demonstrate the superior performance of $f$-DD-based learning algorithms over previous works in popular UDA benchmarks. | https://openreview.net/pdf/e6f6280a04e2892629381753602bc9e403e994ea.pdf |
Improved Generation of Adversarial Examples Against Safety-aligned LLMs | https://openreview.net/forum?id=8hBc843g1p | https://openreview.net/forum?id=8hBc843g1p | Qizhang Li,Yiwen Guo,Wangmeng Zuo,Hao Chen | NIPS 2024,Poster | Adversarial prompts (or say, adversarial examples) generated using gradient-based methods exhibit outstanding performance in performing automatic jailbreak attacks against safety-aligned LLMs. Nevertheless, due to the discrete nature of texts, the input gradient of LLMs struggles to precisely reflect the magnitude of loss change that results from token replacements in the prompt, leading to limited attack success rates against safety-aligned LLMs, even in the *white-box* setting. In this paper, we explore a new perspective on this problem, suggesting that it can be alleviated by leveraging innovations inspired in transfer-based attacks that were originally proposed for attacking *black-box* image classification models. For the first time, we appropriate the ideologies of effective methods among these transfer-based attacks, *i.e.*, Skip Gradient Method and Intermediate Level Attack, into gradient-based adversarial prompt generation and achieve significant performance gains without introducing obvious computational cost. Meanwhile, by discussing mechanisms behind the gains, new insights are drawn, and proper combinations of these methods are also developed. Our empirical results show that 87% of the query-specific adversarial suffixes generated by the developed combination can induce Llama-2-7B-Chat to produce the output that exactly matches the target string on AdvBench. This match rate is 33% higher than that of a very strong baseline known as GCG, demonstrating advanced discrete optimization for adversarial prompt generation against LLMs. In addition, without introducing obvious cost, the combination achieves >30% absolute increase in attack success rates compared with GCG when generating both query-specific (38% ->68%) and universal adversarial prompts (26.68% -> 60.32%) for attacking the Llama-2-7B-Chat model on AdvBench.
Code at: https://github.com/qizhangli/Gradient-based-Jailbreak-Attacks. | https://openreview.net/pdf/993ca7e4d8e5f38ab0bcc37328fa57307b6f0ea9.pdf |
Multi-model Ensemble Conformal Prediction in Dynamic Environments | https://openreview.net/forum?id=J1Y70keorq | https://openreview.net/forum?id=J1Y70keorq | Erfan Hajihashemi,Yanning Shen | NIPS 2024,Poster | Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conformal prediction has been developed to address data distribution shifts in dynamic environments. However, the efficiency of prediction sets varies depending on the learning model used. Employing a single fixed model may not consistently offer the best performance in dynamic environments with unknown data distribution shifts. To address this issue, we introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected ‘on the fly’ from multiple candidate models. The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage. Experiments on both real and synthetic datasets corroborate that the proposed approach consistently yields more efficient prediction sets while maintaining valid coverage, outperforming alternative methods. | https://openreview.net/pdf/e8099609fd67117212f5fdae1d419adf13be51f9.pdf |
Disentangled Representation Learning in Non-Markovian Causal Systems | https://openreview.net/forum?id=uLGyoBn7hm | https://openreview.net/forum?id=uLGyoBn7hm | Adam Li,Yushu Pan,Elias Bareinboim | NIPS 2024,Poster | Considering various data modalities, such as images, videos, and text, humans perform causal reasoning using high-level causal variables, as opposed to operating at the low, pixel level from which the data comes.
In practice, most causal reasoning methods assume that the data is described as granular as the underlying causal generative factors, which is often violated in various AI tasks.
This mismatch translates into a lack of guarantees in various tasks such as generative modeling, decision-making, fairness, and generalizability, to cite a few.
In this paper, we acknowledge this issue and study the problem of causal disentangled representation learning from a combination of data gathered from various heterogeneous domains and assumptions in the form of a latent causal graph. To the best of our knowledge, the proposed work is the first to consider i) non-Markovian causal settings, where there may be unobserved confounding, ii) arbitrary distributions that arise from multiple domains, and iii) a relaxed version of disentanglement. Specifically, we introduce graphical criteria that allow for disentanglement under various conditions. Building on these results, we develop an algorithm that returns a causal disentanglement map, highlighting which latent variables can be disentangled given the combination of data and assumptions. The theory is corroborated by experiments. | https://openreview.net/pdf/8350116f8253990dda7ce413729df73f9a61f109.pdf |
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment | https://openreview.net/forum?id=57OQXxbTbY | https://openreview.net/forum?id=57OQXxbTbY | Teng Xiao,Yige Yuan,Huaisheng Zhu,Mingxiao Li,Vasant G Honavar | NIPS 2024,Poster | We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with available preference data by optimizing the implicit reward associated with the policy. However, the contrastive objective focuses mainly on the relative values of implicit rewards associated with two responses while ignoring
their actual values, resulting in suboptimal alignment with human preferences. To address this limitation, we propose calibrated direct preference optimization (Cal-DPO), a simple yet effective algorithm. We show that substantial improvement in alignment with the given preferences can be achieved simply by calibrating the implicit reward to ensure that the learned implicit rewards are comparable in
scale to the ground-truth rewards. We demonstrate the theoretical advantages of Cal-DPO over existing approaches. The results of our experiments on a variety of standard benchmarks show that Cal-DPO remarkably improves off-the-shelf methods. | https://openreview.net/pdf/deafc46d7f78e13f7390612cd2ea92bd3459b277.pdf |
Stochastic contextual bandits with graph feedback: from independence number to MAS number | https://openreview.net/forum?id=t8iosEWoyd | https://openreview.net/forum?id=t8iosEWoyd | Yuxiao Wen,Yanjun Han,Zhengyuan Zhou | NIPS 2024,Poster | We consider contextual bandits with graph feedback, a class of interactive learning problems with richer structures than vanilla contextual bandits, where taking an action reveals the rewards for all neighboring actions in the feedback graph under all contexts. Unlike the multi-armed bandits setting where a growing literature has painted a near-complete understanding of graph feedback, much remains unexplored in the contextual bandits counterpart. In this paper, we make inroads into this inquiry by establishing a regret lower bound $\Omega(\sqrt{\beta_M(G) T})$, where $M$ is the number of contexts, $G$ is the feedback graph, and $\beta_M(G)$ is our proposed graph-theoretic quantity that characterizes the fundamental learning limit for this class of problems. Interestingly, $\beta_M(G)$ interpolates between $\alpha(G)$ (the independence number of the graph) and $\mathsf{m}(G)$ (the maximum acyclic subgraph (MAS) number of the graph) as the number of contexts $M$ varies. We also provide algorithms that achieve near-optimal regret for important classes of context sequences and/or feedback graphs, such as transitively closed graphs that find applications in auctions and inventory control. In particular, with many contexts, our results show that the MAS number essentially characterizes the statistical complexity for contextual bandits, as opposed to the independence number in multi-armed bandits. | https://openreview.net/pdf/513ccbfe70b63a8134c688af5c125c0ddad739c2.pdf |
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step | https://openreview.net/forum?id=vAOgaPvgYr | https://openreview.net/forum?id=vAOgaPvgYr | Owen M Dugan,Donato M. Jiménez Benetó,Charlotte Loh,Zhuo Chen,Rumen Dangovski,Marin Soljacic | NIPS 2024,Poster | Despite significant advancements in text generation and reasoning, Large Language Models (LLMs) still face challenges in accurately performing complex arithmetic operations. Language model systems often enable LLMs to generate code for arithmetic operations to achieve accurate calculations. However, this approach compromises speed and security, and fine-tuning risks the language model losing prior capabilities. We propose a framework that enables exact arithmetic in *a single autoregressive step*, providing faster, more secure, and more interpretable LLM systems with arithmetic capabilities. We use the hidden states of a LLM to control a symbolic architecture that performs arithmetic. Our implementation using Llama 3 with OccamNet as a symbolic model (OccamLlama) achieves 100\% accuracy on single arithmetic operations ($+,-,\times,\div,\sin{},\cos{},\log{},\exp{},\sqrt{}$), outperforming GPT 4o with and without a code interpreter. Furthermore, OccamLlama outperforms GPT 4o with and without a code interpreter on average across a range of mathematical problem solving benchmarks, demonstrating that OccamLLMs can excel in arithmetic tasks, even surpassing much larger models. Code is available at https://github.com/druidowm/OccamLLM. | https://openreview.net/pdf/2f805a9041d7d2e112fd00bc3259fa9079805498.pdf |
Sample Complexity of Interventional Causal Representation Learning | https://openreview.net/forum?id=XL9aaXl0u6 | https://openreview.net/forum?id=XL9aaXl0u6 | Emre Acartürk,Burak Varıcı,Karthikeyan Shanmugam,Ali Tajer | NIPS 2024,Poster | Consider a data-generation process that transforms low-dimensional _latent_ causally-related variables to high-dimensional _observed_ variables. Causal representation learning (CRL) is the process of using the observed data to recover the latent causal variables and the causal structure among them. Despite the multitude of identifiability results under various interventional CRL settings, the existing guarantees apply exclusively to the _infinite-sample_ regime (i.e., infinite observed samples). This paper establishes the first sample-complexity analysis for the finite-sample regime, in which the interactions between the number of observed samples and probabilistic guarantees on recovering the latent variables and structure are established. This paper focuses on _general_ latent causal models, stochastic _soft_ interventions, and a linear transformation from the latent to the observation space. The identifiability results ensure graph recovery up to ancestors and latent variables recovery up to mixing with parent variables. Specifically, ${\cal O}((\log \frac{1}{\delta})^{4})$ samples suffice for latent graph recovery up to ancestors with probability $1 - \delta$, and ${\cal O}((\frac{1}{\epsilon}\log \frac{1}{\delta})^{4})$ samples suffice for latent causal variables recovery that is $\epsilon$ close to the identifiability class with probability $1 - \delta$. | https://openreview.net/pdf/3cd848f730138b8b2afd1dcc6c71c80ba6f6a6a1.pdf |
On the Complexity of Teaching a Family of Linear Behavior Cloning Learners | https://openreview.net/forum?id=4SAR7IRqmB | https://openreview.net/forum?id=4SAR7IRqmB | Shubham Kumar Bharti,Stephen Wright,Adish Singla,Jerry Zhu | NIPS 2024,Poster | We study optimal teaching for a family of Behavior Cloning learners that learn using a linear hypothesis class. In this setup, a knowledgeable teacher can demonstrate a dataset of state and action tuples and is required to teach an optimal policy to an entire family of BC learners using the smallest possible dataset. We analyze the linear family and design a novel teaching algorithm called `TIE' that achieves the instance optimal Teaching Dimension for the entire family. However, we show that this problem is NP-hard for action spaces with $|\mathcal{A}| > 2$ and provide an efficient approximation algorithm with a $\log(|\mathcal{A}| - 1)$ guarantee on the optimal teaching size. We present empirical results to demonstrate the effectiveness of our algorithm and compare it to various baselines in different teaching environments. | https://openreview.net/pdf/efe5b979cd3307122d731db58d556f69dc10e559.pdf |
Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution | https://openreview.net/forum?id=nRRJsDahEg | https://openreview.net/forum?id=nRRJsDahEg | Yizi Zhang,Yanchen Wang,Donato M. Jiménez-Benetó,Zixuan Wang,Mehdi Azabou,Blake Aaron Richards,Renee Tung,Olivier Winter,International Brain Laboratory,Eva L Dyer,Liam Paninski,Cole Lincoln Hurwitz | NIPS 2024,Poster | Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain Laboratory repeated site dataset, which is comprised of Neuropixels recordings targeting the same brain locations across 48 animals and experimental sessions. The prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding. We demonstrate that our multi-task-masking (MtM) approach significantly improves the performance of current state-of-the-art population models and enables multi-task learning. We also show that by training on multiple animals, we can improve the generalization ability of the model to unseen animals, paving the way for a foundation model of the brain at single-cell, single-spike resolution. | https://openreview.net/pdf/e7664eef58345c56b265804ba3f72932b5f88c14.pdf |
Simple and Effective Masked Diffusion Language Models | https://openreview.net/forum?id=L4uaAR4ArM | https://openreview.net/forum?id=L4uaAR4ArM | Subham Sekhar Sahoo,Marianne Arriola,Aaron Gokaslan,Edgar Mariano Marroquin,Alexander M Rush,Yair Schiff,Justin T Chiu,Volodymyr Kuleshov | NIPS 2024,Poster | While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language modeling.
In this work, we show that simple masked discrete diffusion is more performant than previously thought.
We apply an effective training recipe that improves the performance of masked diffusion models and derive a simplified, Rao-Blackwellized objective that results in additional improvements.
Our objective has a simple form—it is a mixture of classical masked language modeling losses—and can be used to train encoder-only language models that admit efficient samplers, including ones that can generate arbitrary lengths of text semi-autoregressively like a traditional language model.
On language modeling benchmarks, a range of masked diffusion models trained with modern engineering practices achieves a new state-of-the-art among diffusion models, and approaches AR perplexity. We provide the code, along with a blog post and video tutorial on the project page: https://s-sahoo.com/mdlm | https://openreview.net/pdf/3a7ac707cefd8a4120d5e11741324aa678d7ce77.pdf |
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings | https://openreview.net/forum?id=hilGwNabqB | https://openreview.net/forum?id=hilGwNabqB | Disha Makhija,Joydeep Ghosh,Nhat Ho | NIPS 2024,Poster | Federated learning (FL), through its privacy-preserving collaborative learning approach, has significantly empowered decentralized devices. However, constraints in either data and/or computational resources among participating clients introduce several challenges in learning, including the inability to train large model architectures, heightened risks of overfitting, and more. In this work, we present a novel FL framework grounded in Bayesian learning to address these challenges. Our approach involves training personalized Bayesian models at each client tailored to the unique complexities of the clients' datasets and efficiently collaborating across these clients. By leveraging Bayesian neural networks and their uncertainty quantification capabilities, our local training procedure robustly learns from small datasets. And the novel collaboration procedure utilizing priors in the functional (output) space of the networks facilitates collaboration across models of varying sizes, enabling the framework to adapt well in heterogeneous data and computational settings. Furthermore, we present a differentially private version of the algorithm, accompanied by formal differential privacy guarantees that apply without any assumptions on the learning algorithm. Through experiments on popular FL datasets, we demonstrate that our approach outperforms strong baselines in both homogeneous and heterogeneous settings, and under strict privacy constraints. | https://openreview.net/pdf/eae39129243dc6c4b8c87d448599e80d0b9fce05.pdf |
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling | https://openreview.net/forum?id=qZSwlcLMCS | https://openreview.net/forum?id=qZSwlcLMCS | Jiatao Gu,Ying Shen,Shuangfei Zhai,Yizhe Zhang,Navdeep Jaitly,Joshua M. Susskind | NIPS 2024,Poster | Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity in the sampled images, particularly when sampling with a high classifier-free guidance weight. To address this issue, we present Kaleido, a novel approach that enhances the diversity of samples by incorporating autoregressive latent priors. Kaleido integrates an autoregressive language model that encodes the original caption and generates latent variables, serving as abstract and intermediary representations for guiding and facilitating the image generation process.
In this paper, we explore a variety of discrete latent representations, including textual descriptions, detection bounding boxes, object blobs, and visual tokens. These representations diversify and enrich the input conditions to the diffusion models, enabling more diverse outputs.
Our experimental results demonstrate that Kaleido effectively broadens the diversity of the generated image samples from a given textual description while maintaining high image quality. Furthermore, we show that Kaleido adheres closely to the guidance provided by the generated latent variables, demonstrating its capability to effectively control and direct the image generation process. | https://openreview.net/pdf/6bf6fdec8dd6eed85f84157b0809edad3641d855.pdf |
FairWire: Fair Graph Generation | https://openreview.net/forum?id=V0JvwCQlJe | https://openreview.net/forum?id=V0JvwCQlJe | Oyku Deniz Kose,Yanning Shen | NIPS 2024,Poster | Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for their deployment in real-world decision systems. In addition, while synthetic graph generation has become pivotal for privacy and scalability considerations, the impact of generative learning algorithms on structural bias has not yet been investigated. Motivated by this, this work focuses on the analysis and mitigation of structural bias for both real and synthetic graphs. Specifically, we first theoretically analyze the sources of structural bias that result in disparity for the predictions of dyadic relations. To alleviate the identified bias factors, we design a novel fairness regularizer that offers a versatile use. Faced with the bias amplification in graph generation models brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model. Experimental results on real-world networks validate that the proposed tools herein deliver effective structural bias mitigation for both real and synthetic graphs. | https://openreview.net/pdf/1f5eea2983c84e12175cd2b978aa11cb3f7ce158.pdf |
Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans | https://openreview.net/forum?id=pwRVGRWtGg | https://openreview.net/forum?id=pwRVGRWtGg | Jen-tse Huang,Man Ho LAM,Eric John Li,Shujie Ren,Wenxuan Wang,Wenxiang Jiao,Zhaopeng Tu,Michael Lyu | NIPS 2024,Poster | Evaluating Large Language Models’ (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a careful and comprehensive survey, we collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. Categorizing the situations into 36 factors, we conduct a human evaluation involving more than 1,200 subjects worldwide. With the human evaluation results as references, our evaluation includes seven LLMs, covering both commercial and open-source models, including variations in model sizes, featuring the latest iterations, such as GPT-4, Mixtral-8x22B, and LLaMA-3.1. We find that, despite several misalignments, LLMs can generally respond appropriately to certain situations. Nevertheless, they fall short in alignment with the emotional behaviors of human beings and cannot establish connections between similar situations. Our collected dataset of situations, the human evaluation results, and the code of our testing framework, i.e., EmotionBench, are publicly available at https://github.com/CUHK-ARISE/EmotionBench. | https://openreview.net/pdf/4d6e71e0ca7fffae0c70fd69763ea99167e3d197.pdf |
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting | https://openreview.net/forum?id=aBP01akha9 | https://openreview.net/forum?id=aBP01akha9 | Tung Nguyen,Rohan Shah,Hritik Bansal,Troy Arcomano,Romit Maulik,Veerabhadra Kotamarthi,Ian Foster,Sandeep Madireddy,Aditya Grover | NIPS 2024,Poster | Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer’s favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens. Code and checkpoints are available at https://github.com/tung-nd/stormer. | https://openreview.net/pdf/2fdb23d735460d3e9df36b9d966b324b7a000548.pdf |
A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning | https://openreview.net/forum?id=3tj3A26wsV | https://openreview.net/forum?id=3tj3A26wsV | Tom Yan,Zachary Chase Lipton | NIPS 2024,Poster | A key source of complexity in next-generation AI models is the size of model outputs, making it time-consuming to parse and provide reliable feedback on. To ensure such models are aligned, we will need to bolster our understanding of scalable oversight and how to scale up human feedback. To this end, we study the challenges of scalable oversight in the context of goal-conditioned hierarchical reinforcement learning. Hierarchical structure is a promising entrypoint into studying how to scale up human feedback, which in this work we assume can only be provided for model outputs below a threshold size. In the cardinal feedback setting, we develop an apt sub-MDP reward and algorithm that allows us to acquire and scale up low-level feedback for learning with sublinear regret. In the ordinal feedback setting, we show the necessity of both high- and low-level feedback, and develop a hierarchical experimental design algorithm that efficiently acquires both types of feedback for learning. Altogether, our work aims to consolidate the foundations of scalable oversight, formalizing and studying the various challenges thereof. | https://openreview.net/pdf/a143b9d7d28c1a6e8cdea9a18adc4fa9293ed1a7.pdf |
Causal Imitation for Markov Decision Processes: a Partial Identification Approach | https://openreview.net/forum?id=KHX0dKXdqH | https://openreview.net/forum?id=KHX0dKXdqH | Kangrui Ruan,Junzhe Zhang,Xuan Di,Elias Bareinboim | NIPS 2024,Poster | Imitation learning enables an agent to learn from expert demonstrations when the performance measure is unknown and the reward signal is not specified. Standard imitation methods do not generally apply when the learner and the expert's sensory capabilities mismatch and demonstrations are contaminated with unobserved confounding bias. To address these challenges, recent advancements in causal imitation learning have been pursued. However, these methods often require access to underlying causal structures that might not always be available, posing practical challenges.
In this paper, we investigate robust imitation learning within the framework of canonical Markov Decision Processes (MDPs) using partial identification, allowing the agent to achieve expert performance even when the system dynamics are not uniquely determined from the confounded expert demonstrations. Specifically, first, we theoretically demonstrate that when unobserved confounders (UCs) exist in an MDP, the learner is generally unable to imitate expert performance. We then explore imitation learning in partially identifiable settings --- either transition distribution or reward function is non-identifiable from the available data and knowledge. Augmenting the celebrated GAIL method (Ho \& Ermon, 2016), our analysis leads to two novel causal imitation algorithms that can obtain effective policies guaranteed to achieve expert performance. | https://openreview.net/pdf/44332f130be85fa6cd9ebf2c17a3b40392bccbae.pdf |
Learning from Uncertain Data: From Possible Worlds to Possible Models | https://openreview.net/forum?id=v9RqRFSLQ2 | https://openreview.net/forum?id=v9RqRFSLQ2 | Jiongli Zhu,Su Feng,Boris Glavic,Babak Salimi | NIPS 2024,Poster | We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity. Our approach leverages abstract interpretation and zonotopes, a type of convex polytope, to compactly represent these dataset variations, enabling the symbolic execution of gradient descent on all possible worlds simultaneously. We develop techniques to ensure that this process converges to a fixed point and derive closed-form solutions for this fixed point. Our method provides sound over-approximations of all possible optimal models and viable prediction ranges. We demonstrate the effectiveness of our approach through theoretical and empirical analysis, highlighting its potential to reason about model and prediction uncertainty due to data quality issues in training data. | https://openreview.net/pdf/87fd59808dc5ed78d3e3e6ef14d35b6e060362d8.pdf |
Adaptive Exploration for Data-Efficient General Value Function Evaluations | https://openreview.net/forum?id=HC6iqpPt3L | https://openreview.net/forum?id=HC6iqpPt3L | Arushi Jain,Josiah P. Hanna,Doina Precup | NIPS 2024,Poster | General Value Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning. Each GVF computes the expected return for a given policy, based on a unique reward. Existing methods relying on fixed behavior policies or pre-collected data often face data efficiency issues when learning multiple GVFs in parallel using off-policy methods. To address this, we introduce *GVFExplorer*, which adaptively learns a single behavior policy that efficiently collects data for evaluating multiple GVFs in parallel. Our method optimizes the behavior policy by minimizing the total variance in return across GVFs, thereby reducing the required environmental interactions. We use an existing temporal-difference-style variance estimator to approximate the return variance. We prove that each behavior policy update decreases the overall mean squared error in GVF predictions. We empirically show our method's performance in tabular and nonlinear function approximation settings, including Mujoco environments, with stationary and non-stationary reward signals, optimizing data usage and reducing prediction errors across multiple GVFs. | https://openreview.net/pdf/20c5e327d236868140f6e856c42c6b8592a50482.pdf |
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context | https://openreview.net/forum?id=WDX45LNZXE | https://openreview.net/forum?id=WDX45LNZXE | Zihao Li,Yuan Cao,Cheng Gao,Yihan He,Han Liu,Jason Matthew Klusowski,Jianqing Fan,Mengdi Wang | NIPS 2024,Poster | Transformers have achieved great success in recent years. Interestingly, transformers have shown particularly strong in-context learning capability -- even without fine-tuning, they are still able to solve unseen tasks well purely based on task-specific prompts. In this paper, we study the capability of one-layer transformers in learning the one-nearest neighbor prediction rule. Under a theoretical framework where the prompt contains a sequence of labeled training data and unlabeled test data, we show that, although the loss function is nonconvex, when trained with gradient descent, a single softmax attention layer can successfully learn to behave like a one-nearest neighbor classifier. Our result gives a concrete example on how transformers can be trained to implement nonparametric machine learning algorithms, and sheds light on the role of softmax attention in transformer models. | https://openreview.net/pdf/69e3d7430a05e0d5696f5dbe23746ff3a22096e9.pdf |
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation | https://openreview.net/forum?id=65UoJ0z7Kp | https://openreview.net/forum?id=65UoJ0z7Kp | Yixia Li,Boya Xiong,Guanhua Chen,Yun Chen | NIPS 2024,Poster | Out-of-distribution (OOD) detection is crucial for the safe deployment of neural networks. Existing CLIP-based approaches perform OOD detection by devising novel scoring functions or sophisticated fine-tuning methods. In this work, we propose SeTAR, a novel, training-free OOD detection method that leverages selective low-rank approximation of weight matrices in vision-language and vision-only models. SeTAR enhances OOD detection via post-hoc modification of the model's weight matrices using a simple greedy search algorithm. Based on SeTAR, we further propose SeTAR+FT, a fine-tuning extension optimizing model performance for OOD detection tasks. Extensive evaluations on ImageNet1K and Pascal-VOC benchmarks show SeTAR's superior performance, reducing the relatively false positive rate by up to 18.95\% and 36.80\% compared to zero-shot and fine-tuning baselines. Ablation studies further validate our approach's effectiveness, robustness, and generalizability across different model backbones. Our work offers a scalable, efficient solution for OOD detection, setting a new state-of-the-art in this area. | https://openreview.net/pdf/1f941a564d6513eccbbda6e05d521e80daf9ffcc.pdf |
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations | https://openreview.net/forum?id=MzNjnbgcPN | https://openreview.net/forum?id=MzNjnbgcPN | Yao Shu,Jiongfeng Fang,Ying Tiffany He,Fei Richard Yu | NIPS 2024,Poster | First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence. In response, we introduce first-order optimization expedited with approximately parallelized iterations (OptEx), the first general framework that enhances the time efficiency of FOO by leveraging parallel computing to directly mitigate its requirement of many sequential iterations for convergence. To achieve this, OptEx utilizes a kernelized gradient estimation that is based on the history of evaluated gradients to predict the gradients required by the next few sequential iterations in FOO, which helps to break the inherent iterative dependency and hence enables the approximate parallelization of iterations in FOO. We further establish theoretical guarantees for the estimation error of our kernelized gradient estimation and the iteration complexity of SGD-based OptEx, confirming that the estimation error diminishes to zero as the history of gradients accumulates and that our SGD-based OptEx enjoys an effective acceleration rate of Θ(√N ) over standard SGD given parallelism of N, in terms of the sequential iterations required for convergence. Finally, we provide extensive empirical studies, including synthetic functions, reinforcement learning tasks, and neural network training on various datasets, to underscore the substantial efficiency improvements achieved by our OptEx in practice. | https://openreview.net/pdf/6355a6b19af5c8832921ce57986888808909ddc1.pdf |
FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained Understanding | https://openreview.net/forum?id=nExI4FuKWD | https://openreview.net/forum?id=nExI4FuKWD | Dong Jing,Xiaolong He,Yutian Luo,Nanyi Fei,Guoxing Yang,Wei Wei,Huiwen Zhao,Zhiwu Lu | NIPS 2024,Poster | Contrastive Language-Image Pre-training (CLIP) achieves impressive performance on tasks like image classification and image-text retrieval by learning on large-scale image-text datasets. However, CLIP struggles with dense prediction tasks due to the poor grasp of the fine-grained details. Although existing works pay attention to this issue, they achieve limited improvements and usually sacrifice the important visual-semantic consistency. To overcome these limitations, we propose FineCLIP, which keeps the global contrastive learning to preserve the visual-semantic consistency and further enhances the fine-grained understanding through two innovations: 1) A real-time self-distillation scheme that facilitates the transfer of representation capability from global to local features. 2) A semantically-rich regional contrastive learning paradigm with generated region-text pairs, boosting the local representation capabilities with abundant fine-grained knowledge.
Both cooperate to fully leverage diverse semantics and multi-grained complementary information.
To validate the superiority of our FineCLIP and the rationality of each design, we conduct extensive experiments on challenging dense prediction and image-level tasks.
All the observations demonstrate the effectiveness of FineCLIP. | https://openreview.net/pdf/e77b9bf69974b22ae77ee4209dc907d97148cbdd.pdf |
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games | https://openreview.net/forum?id=QgMC8ftbNd | https://openreview.net/forum?id=QgMC8ftbNd | Awni Altabaa,Zhuoran Yang | NIPS 2024,Poster | In sequential decision-making problems, the *information structure* describes the causal dependencies between system variables, encompassing the dynamics of the environment and the agents' actions. Classical models of reinforcement learning (e.g., MDPs, POMDPs) assume a restricted and highly regular information structure, while more general models like predictive state representations do not explicitly model the information structure. By contrast, real-world sequential decision-making problems typically involve a complex and time-varying interdependence of system variables, requiring a rich and flexible representation of information structure. In this paper, we formalize a novel reinforcement learning model which explicitly represents the information structure.
We then use this model to carry out an information-structural analysis of the statistical complexity of general sequential decision-making problems, obtaining a characterization via a graph-theoretic quantity of the DAG representation of the information structure. We prove an upper bound on the sample complexity of learning a general sequential decision-making problem in terms of its information structure by exhibiting an algorithm achieving the upper bound. This recovers known tractability results and gives a novel perspective on reinforcement learning in general sequential decision-making problems, providing a systematic way of identifying new tractable classes of problems. | https://openreview.net/pdf/48f357d190f35342349977f6fc217aacfb61f634.pdf |
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection | https://openreview.net/forum?id=QNieOPt4fg | https://openreview.net/forum?id=QNieOPt4fg | Liangxin Liu,Xuebo Liu,Derek F. Wong,Dongfang Li,Ziyi Wang,Baotian Hu,Min Zhang | NIPS 2024,Poster | Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a small, high-quality subset of IT data can significantly enhance the performance of LLMs. Despite this, common approaches often rely on additional models or data, which increases costs and limits widespread adoption. In this work, we propose a novel approach, termed $\textit{SelectIT}$, that capitalizes on the foundational capabilities of the LLM itself. Specifically, we exploit the intrinsic uncertainty present in LLMs to more effectively select high-quality IT data, without the need for extra resources. Furthermore, we introduce a curated IT dataset, the $\textit{Selective Alpaca}$, created by applying SelectIT to the Alpaca-GPT4 dataset. Empirical results demonstrate that IT using Selective Alpaca leads to substantial model ability enhancement. The robustness of SelectIT has also been corroborated in various foundation models and domain-specific tasks. Our findings suggest that longer and more computationally intensive IT data may serve as superior sources of IT, offering valuable insights for future research in this area. Data, code, and scripts are freely available at https://github.com/Blue-Raincoat/SelectIT. | https://openreview.net/pdf/9ee81561e94050705f358e4b646c204f4ac6cb24.pdf |
Aligning Large Language Models with Representation Editing: A Control Perspective | https://openreview.net/forum?id=yTTomSJsSW | https://openreview.net/forum?id=yTTomSJsSW | Lingkai Kong,Haorui Wang,Wenhao Mu,Yuanqi Du,Yuchen Zhuang,Yifei Zhou,Yue Song,Rongzhi Zhang,Kai Wang,Chao Zhang | NIPS 2024,Poster | Aligning large language models (LLMs) with human objectives is crucial for real-world applications. However, fine-tuning LLMs for alignment often suffers from unstable training and requires substantial computing resources. Test-time alignment techniques, such as prompting and guided decoding, do not modify the underlying model, and their performance remains dependent on the original model's capabilities. To address these challenges, we propose aligning LLMs through representation editing. The core of our method is to view a pre-trained autoregressive LLM as a discrete-time stochastic dynamical system. To achieve alignment for specific objectives, we introduce external control signals into the state space of this language dynamical system. We train a value function directly on the hidden states according to the Bellman equation, enabling gradient-based optimization to obtain the optimal control signals at test time. Our experiments demonstrate that our method outperforms existing test-time alignment techniques while requiring significantly fewer resources compared to fine-tuning methods. Our code is available at [https://github.com/Lingkai-Kong/RE-Control](https://github.com/Lingkai-Kong/RE-Control). | https://openreview.net/pdf/5b01199621eef2e71cc22c61871a279fc51beeba.pdf |
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning | https://openreview.net/forum?id=cCDMXXiamP | https://openreview.net/forum?id=cCDMXXiamP | Meenatchi Sundaram Muthu Selva Annamalai,Emiliano De Cristofaro | NIPS 2024,Poster | This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work.
The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agnostic to the choice of the initial model parameters.
For models trained on MNIST and CIFAR-10 at theoretical $\varepsilon=10.0$, our auditing procedure yields empirical estimates of $\varepsilon_{emp} = 7.21$ and $6.95$, respectively, on a 1,000-record sample and $\varepsilon_{emp} = 6.48$ and $4.96$ on the full datasets.
By contrast, previous audits were only (relatively) tight in stronger white-box models, where the adversary can access the model's inner parameters and insert arbitrary gradients.
Overall, our auditing procedure can offer valuable insight into how the privacy analysis of DP-SGD could be improved and detect bugs and DP violations in real-world implementations.
The source code needed to reproduce our experiments is available from https://github.com/spalabucr/bb-audit-dpsgd. | https://openreview.net/pdf/4b8080bdff94b173112c6cc0c6042066baef4b32.pdf |
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training | https://openreview.net/forum?id=Gug7wc0BSs | https://openreview.net/forum?id=Gug7wc0BSs | Pihe Hu,Shaolong Li,Zhuoran Li,Ling Pan,Longbo Huang | NIPS 2024,Poster | Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Consequently, there is an urgent need to expedite training and enable model compression in MARL. This paper proposes the utilization of dynamic sparse training (DST), a technique proven effective in deep supervised learning tasks, to alleviate the computational burdens in MARL training. However, a direct adoption of DST fails to yield satisfactory MARL agents, leading to breakdowns in value learning within deep sparse value-based MARL models. Motivated by this challenge, we introduce an innovative Multi-Agent Sparse Training (MAST) framework aimed at simultaneously enhancing the reliability of learning targets and the rationality of sample distribution to improve value learning in sparse models. Specifically, MAST incorporates the Soft Mellowmax Operator with a hybrid TD-($\lambda$) schema to establish dependable learning targets. Additionally, it employs a dual replay buffer mechanism to enhance the distribution of training samples. Building upon these aspects, MAST utilizes gradient-based topology evolution to exclusively train multiple MARL agents using sparse networks. Our comprehensive experimental investigation across various value-based MARL algorithms on multiple benchmarks demonstrates, for the first time, significant reductions in redundancy of up to $20\times$ in Floating Point Operations (FLOPs) for both training and inference, with less than 3% performance degradation. | https://openreview.net/pdf/bf8bb00ab8e48a246aea7bd4371261f2f92f54dd.pdf |
Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame | https://openreview.net/forum?id=z4FaPUslma | https://openreview.net/forum?id=z4FaPUslma | Evan Markou,Thalaiyasingam Ajanthan,Stephen Gould | NIPS 2024,Poster | Neural Collapse (NC) is a recently observed phenomenon in neural networks that characterises the solution space of the final classifier layer when trained until zero training loss. Specifically, NC suggests that the final classifier layer converges to a Simplex Equiangular Tight Frame (ETF), which maximally separates the weights corresponding to each class. By duality, the penultimate layer feature means also converge to the same simplex ETF. Since this simple symmetric structure is optimal, our idea is to utilise this property to improve convergence speed. Specifically, we introduce the notion of \textit{nearest simplex ETF geometry} for the penultimate layer features at any given training iteration, by formulating it as a Riemannian optimisation. Then, at each iteration, the classifier weights are implicitly set to the nearest simplex ETF by solving this inner-optimisation, which is encapsulated within a declarative node to allow backpropagation. Our experiments on synthetic and real-world architectures on classification tasks demonstrate that our approach accelerates convergence and enhances training stability. | https://openreview.net/pdf/ac02c11fa162633bf19fadb27beddf13e3c58e97.pdf |
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit | https://openreview.net/forum?id=rblaF2euXQ | https://openreview.net/forum?id=rblaF2euXQ | Seok-Jin Kim,Min-hwan Oh | NIPS 2024,Poster | We study the performance guarantees of exploration-free greedy algorithms for the linear contextual bandit problem.
We introduce a novel condition, named the \textit{Local Anti-Concentration} (LAC) condition, which enables a greedy bandit algorithm to achieve provable efficiency.
We show that the LAC condition is satisfied by a broad class of distributions, including Gaussian, exponential, uniform, Cauchy, and Student's~$t$ distributions, along with other exponential family distributions and their truncated variants.
This significantly expands the class of distributions under which greedy algorithms can perform efficiently.
Under our proposed LAC condition, we prove that the cumulative expected regret of the greedy algorithm for the linear contextual bandit is bounded by $\mathcal{O}(\operatorname{poly} \log T)$.
Our results establish the widest range of distributions known to date that allow a sublinear regret bound for greedy algorithms, further achieving a sharp poly-logarithmic regret. | https://openreview.net/pdf/4de6a468dddbfb975396e4c31c95c83e157b2eae.pdf |
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging | https://openreview.net/forum?id=U4WeoyRHPd | https://openreview.net/forum?id=U4WeoyRHPd | Zhenghao Pan,Haijin Zeng,Jiezhang Cao,Yongyong Chen,Kai Zhang,Yong Xu | NIPS 2024,Poster | Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With the increasing popularity of quad-Bayer pattern in mainstream smartphone cameras for capturing high-resolution videos, mobile photography has become more accessible to a wider audience. However, existing color video SCI reconstruction algorithms are designed based on the traditional Bayer pattern. When applied to videos captured by quad-Bayer cameras, these algorithms often result in color distortion and ineffective demosaicing, rendering them impractical for primary equipment. To address this challenge, we propose the MambaSCI method, which leverages the Mamba and UNet architectures for efficient reconstruction of quad-Bayer patterned color video SCI. To the best of our knowledge, our work presents the first algorithm for quad-Bayer patterned SCI reconstruction, and also the initial application of the Mamba model to this task. Specifically, we customize Residual-Mamba-Blocks, which residually connect the Spatial-Temporal Mamba (STMamba), Edge-Detail-Reconstruction (EDR) module, and Channel Attention (CA) module. Respectively, STMamba is used to model long-range spatial-temporal dependencies with linear complexity, EDR is for better edge-detail reconstruction, and CA is used to compensate for the missing channel information interaction in Mamba model. Experiments demonstrate that MambaSCI surpasses state-of-the-art methods with lower computational and memory costs. PyTorch style pseudo-code for the core modules is provided in the supplementary materials. Code is at https://github.com/PAN083/MambaSCI. | https://openreview.net/pdf/72c3ea7ea0eeba8e9718d04e2061a016d54bfee0.pdf |
KnowGPT: Knowledge Graph based Prompting for Large Language Models | https://openreview.net/forum?id=PacBluO5m7 | https://openreview.net/forum?id=PacBluO5m7 | Qinggang Zhang,Junnan Dong,Hao Chen,Daochen Zha,Zailiang Yu,Xiao Huang | NIPS 2024,Poster | Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce hallucinations, wherein the models fabricate incorrect statements on tasks beyond their knowledge and perception. To alleviate this issue, graph retrieval-augmented generation (GraphRAG) has been extensively explored which leverages the factual knowledge in knowledge graphs (KGs) to ground the LLM's responses in established facts and principles. However, most state-of-the-art LLMs are closed-source, making it challenging to develop a prompting framework that can efficiently and effectively integrate KGs into LLMs with hard prompts only. Generally, existing KG-enhanced LLMs usually suffer from three critical issues, including huge search space, high API costs, and laborious prompt engineering, that impede their widespread application in practice. To this end, we introduce a novel **Know**ledge **Gr**aph based **P**romp**T**ing framework, namely **KnowGPT**, to enhance LLMs with domain knowledge. KnowGPT contains a knowledge extraction module to extract the most informative knowledge from KGs, and a context-aware prompt construction module to automatically convert extracted knowledge into effective prompts. Experiments on three benchmarks demonstrate that KnowGPT significantly outperforms all competitors. Notably, KnowGPT achieves a 92.6% accuracy on OpenbookQA leaderboard, comparable to human-level performance. | https://openreview.net/pdf/4ec9739895ff72a71118e7b64bb98e28f109616b.pdf |
Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments | https://openreview.net/forum?id=Y1rOWS2Z4i | https://openreview.net/forum?id=Y1rOWS2Z4i | Siddharth Nayak,Adelmo Morrison Orozco,Marina Ten Have,Jackson Zhang,Vittal Thirumalai,Darren Chen,Aditya Kapoor,Eric Robinson,Karthik Gopalakrishnan,James Harrison,Anuj Mahajan,brian ichter,Hamsa Balakrishnan | NIPS 2024,Poster | The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30\% higher success rate than other state-of-the-art LM-based multi-agent planners in MAP-THOR and Search \& Rescue tasks. Code can be found at [https://github.com/nsidn98/LLaMAR](https://github.com/nsidn98/LLaMAR) | https://openreview.net/pdf/c6dfb94fb019cc2a364a5a1bc89c8064812a935d.pdf |
Cost-efficient Knowledge-based Question Answering with Large Language Models | https://openreview.net/forum?id=pje1Y71jad | https://openreview.net/forum?id=pje1Y71jad | Junnan Dong,Qinggang Zhang,Chuang Zhou,Hao Chen,Daochen Zha,Xiao Huang | NIPS 2024,Poster | Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving. However, it remains challenging since accuracy and cost are not readily combined in the optimization as two distinct metrics. It is also laborious for model selection since different models excel in diverse knowledge. To this end, we propose Coke, a novel cost-efficient strategy for KBQA with LLMs, modeled as a tailored multi-armed bandit problem to minimize calls to LLMs within limited budgets. We first formulate the accuracy expectation with a cluster-level Thompson Sampling for either KGMs or LLMs. A context-aware policy is optimized to further distinguish the expert model subject to the question semantics. The overall decision is bounded by the cost regret according to historical expenditure on failures. Extensive experiments showcase the superior performance of Coke, which moves the Pareto frontier with up to 20.89% saving of GPT-4 fees while achieving a 2.74% higher accuracy on the benchmark datasets. | https://openreview.net/pdf/0c4dc789433d497c2f2c0f0da165be3b5c9f715b.pdf |
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes | https://openreview.net/forum?id=da0ZJatRCN | https://openreview.net/forum?id=da0ZJatRCN | Syrine Belakaria,Benjamin Letham,Jana Doppa,Barbara E Engelhardt,Stefano Ermon,Eytan Bakshy | NIPS 2024,Poster | We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input variables, e.g., in vehicle safety experimentation, we study the impact of the thickness of various components on safety objectives. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models.
We showcase the first application of active learning directly to DGSMs, and develop tractable uncertainty reduction and information gain acquisition functions for these measures. Through comprehensive evaluation on synthetic and real-world problems, our study demonstrates how these active learning acquisition strategies substantially enhance the sample efficiency of DGSM estimation, particularly with limited evaluation budgets. Our work paves the way for more efficient and accurate sensitivity analysis in various scientific and engineering applications. | https://openreview.net/pdf/e7cbe6f405410f66193a63a86f7ddaae0e3eb870.pdf |
Divergences between Language Models and Human Brains | https://openreview.net/forum?id=DpP5F3UfKw | https://openreview.net/forum?id=DpP5F3UfKw | Yuchen Zhou,Emmy Liu,Graham Neubig,Michael J. Tarr,Leila Wehbe | NIPS 2024,Poster | Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories. Using an LLM-based data-driven approach, we identify two domains that LMs do not capture well: social/emotional intelligence and physical commonsense. We validate these findings with human behavioral experiments and hypothesize that the gap is due to insufficient representations of social/emotional and physical knowledge in LMs. Our results show that fine-tuning LMs on these domains can improve their alignment with human brain responses. | https://openreview.net/pdf/3f5c514423f1a9678561f73def188118d5bcf7d3.pdf |
Covariate Shift Corrected Conditional Randomization Test | https://openreview.net/forum?id=Me5esZTRqW | https://openreview.net/forum?id=Me5esZTRqW | Bowen Xu,Yiwen Huang,Chuan Hong,Shuangning Li,Molei Liu | NIPS 2024,Poster | Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable $Y$ from a treatment variable $X$, conditioning on a set of confounders $Z$. The Conditional Randomization Test (CRT) offers a powerful framework for such testing by assuming known distributions of $X \mid Z$; it controls the Type-I error exactly, allowing for the use of flexible, black-box test statistics. In practice, testing for conditional independence often involves using data from a source population to draw conclusions about a target population. This can be challenging due to covariate shift---differences in the distribution of $X$, $Z$, and surrogate variables, which can affect the conditional distribution of $Y \mid X, Z$---rendering traditional CRT approaches invalid. To address this issue, we propose a novel Covariate Shift Corrected Pearson Chi-squared Conditional Randomization (csPCR) test. This test adapts to covariate shifts by integrating importance weights and employing the control variates method to reduce variance in the test statistics and thus enhance power. Theoretically, we establish that the csPCR test controls the Type-I error asymptotically. Empirically, through simulation studies, we demonstrate that our method not only maintains control over Type-I errors but also exhibits superior power, confirming its efficacy and practical utility in real-world scenarios where covariate shifts are prevalent. Finally, we apply our methodology to a real-world dataset to assess the impact of a COVID-19 treatment on the 90-day mortality rate among patients. | https://openreview.net/pdf/74507c2335d3c2e774426629dc05a2f7ad13d3bb.pdf |
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context | https://openreview.net/forum?id=uHcG5Y6fdB | https://openreview.net/forum?id=uHcG5Y6fdB | Kazusato Oko,Yujin Song,Taiji Suzuki,Denny Wu | NIPS 2024,Poster | Transformers can efficiently learn in-context from example demonstrations. Most existing theoretical analyses studied the in-context learning (ICL) ability of transformers for linear function classes, where it is typically shown that the minimizer of the pretraining loss implements one gradient descent step on the least squares objective. However, this simplified linear setting arguably does not demonstrate the statistical efficiency of ICL, since the pretrained transformer does not outperform directly solving linear regression on the test prompt.
In this paper, we study ICL of a nonlinear function class via transformer with nonlinear MLP layer: given a class of \textit{single-index} target functions $f_*(\boldsymbol{x}) = \sigma_*(\langle\boldsymbol{x},\boldsymbol{\beta}\rangle)$, where the index features $\boldsymbol{\beta}\in\mathbb{R}^d$ are drawn from a $r$-dimensional subspace, we show that a nonlinear transformer optimized by gradient descent (with a pretraining sample complexity that depends on the \textit{information exponent} of the link functions $\sigma_*$) learns $f_*$ in-context with a prompt length that only depends on the dimension of the distribution of target functions $r$; in contrast, any algorithm that directly learns $f_*$ on test prompt yields a statistical complexity that scales with the ambient dimension $d$. Our result highlights the adaptivity of the pretrained transformer to low-dimensional structures of the function class, which enables sample-efficient ICL that outperforms estimators that only have access to the in-context data. | https://openreview.net/pdf/ebe3fdc5e357d327b920801545a353f902eefb86.pdf |
An effective framework for estimating individualized treatment rules | https://openreview.net/forum?id=G7L65B2P0y | https://openreview.net/forum?id=G7L65B2P0y | Joowon Lee,Jared Davis Huling,Guanhua Chen | NIPS 2024,Poster | Estimating individualized treatment rules (ITRs) is fundamental in causal inference, particularly for precision medicine applications. Traditional ITR estimation methods rely on inverse probability weighting (IPW) to address confounding factors and $L_{1}$-penalization for simplicity and interpretability. However, IPW can introduce statistical bias without precise propensity score modeling, while $L_1$-penalization makes the objective non-smooth, leading to computational bias and requiring subgradient methods. In this paper, we propose a unified ITR estimation framework formulated as a constrained, weighted, and smooth convex optimization problem. The optimal ITR can be robustly and effectively computed by projected gradient descent. Our comprehensive theoretical analysis reveals that weights that balance the spectrum of a `weighted design matrix' improve both the optimization and likelihood landscapes, yielding improved computational and statistical estimation guarantees. In particular, this is achieved by distributional covariate balancing weights, which are model-free alternatives to IPW. Extensive simulations and applications demonstrate that our framework achieves significant gains in both robustness and effectiveness for ITR learning against existing methods. | https://openreview.net/pdf/3b195f1ab7b8f324455c2d592ed796416f102aeb.pdf |
DiMSUM: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation | https://openreview.net/forum?id=KqbLzSIXkm | https://openreview.net/forum?id=KqbLzSIXkm | Hao Phung,Quan Dao,Trung Tuan Dao,Hoang Phan,Dimitris N. Metaxas,Anh Tuan Tran | NIPS 2024,Poster | We introduce a novel state-space architecture for diffusion models, effectively harnessing spatial and frequency information to enhance the inductive bias towards local features in input images for image generation tasks. While state-space networks, including Mamba, a revolutionary advancement in recurrent neural networks, typically scan input sequences from left to right, they face difficulties in designing effective scanning strategies, especially in the processing of image data. Our method demonstrates that integrating wavelet transformation into Mamba enhances the local structure awareness of visual inputs and better captures long-range relations of frequencies by disentangling them into wavelet subbands, representing both low- and high-frequency components. These wavelet-based outputs are then processed and seamlessly fused with the original Mamba outputs through a cross-attention fusion layer, combining both spatial and frequency information to optimize the order awareness of state-space models which is essential for the details and overall quality of image generation. Besides, we introduce a globally-shared transformer to supercharge the performance of Mamba, harnessing its exceptional power to capture global relationships. Through extensive experiments on standard benchmarks, our method demonstrates superior results compared to DiT and DIFFUSSM, achieving faster training convergence and delivering high-quality outputs. The codes and pretrained models are released at https://github.com/VinAIResearch/DiMSUM.git. | https://openreview.net/pdf/0ae8bfdeeec0ac6c1b9be00728313d0eee7040d2.pdf |
Rule Based Rewards for Language Model Safety | https://openreview.net/forum?id=QVtwpT5Dmg | https://openreview.net/forum?id=QVtwpT5Dmg | Tong Mu,Alec Helyar,Johannes Heidecke,Joshua Achiam,Andrea Vallone,Ian D Kivlichan,Molly Lin,Alex Beutel,John Schulman,Lilian Weng | NIPS 2024,Poster | Reinforcement learning based fine-tuning of large language models (LLMs) on human preferences has been shown to enhance both their capabilities and safety behavior.
However, in cases related to safety, without precise instructions to human annotators, the data collected may cause the model to become overly cautious, or to respond in an undesirable style, such as being judgmental.
Additionally, as model capabilities and usage patterns evolve, there may be a costly need to add or relabel data to modify safety behavior.
We propose a novel preference modeling approach that utilizes AI feedback and only requires a small amount of human data.
Our method, Rule Based Rewards (RBR), uses a collection of rules for desired or undesired behaviors (e.g. refusals should not be judgmental) along with a LLM grader.
In contrast to prior methods using AI feedback, our method uses fine-grained, composable, LLM-graded few-shot prompts as reward directly in RL training, resulting in greater control, accuracy and ease of updating.
We show that RBRs are an effective training method, achieving an F1 score of 97.1, compared to a human-feedback baseline of 91.7, resulting in much higher safety-behavior accuracy through better balancing usefulness and safety. | https://openreview.net/pdf/e963b11386699f5b75503a72861c8a01fb09a180.pdf |
Alias-Free Mamba Neural Operator | https://openreview.net/forum?id=gUEBXGV8JM | https://openreview.net/forum?id=gUEBXGV8JM | Jianwei Zheng,LiweiNo,Ni Xu,Junwei Zhu,XiaoxuLin,Xiaoqin Zhang | NIPS 2024,Poster | Benefiting from the booming deep learning techniques, neural operators (NO) are considered as an ideal alternative to break the traditions of solving Partial Differential Equations (PDE) with expensive cost.
Yet with the remarkable progress, current solutions concern little on the holistic function features--both global and local information-- during the process of solving PDEs.
Besides, a meticulously designed kernel integration to meet desirable performance often suffers from a severe computational burden, such as GNO with $O(N(N-1))$, FNO with $O(NlogN)$, and Transformer-based NO with $O(N^2)$.
To counteract the dilemma, we propose a mamba neural operator with $O(N)$ computational complexity, namely MambaNO.
Functionally, MambaNO achieves a clever balance between global integration, facilitated by state space model of Mamba that scans the entire function, and local integration, engaged with an alias-free architecture. We prove a property of continuous-discrete equivalence to show the capability of
MambaNO in approximating operators arising from universal PDEs to desired accuracy. MambaNOs are evaluated on a diverse set of benchmarks with possibly multi-scale solutions and set new state-of-the-art scores, yet with fewer parameters and better efficiency. | https://openreview.net/pdf/a1a1561a826925c5f0083b9694af193271f8b359.pdf |
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions | https://openreview.net/forum?id=x7usmidzxj | https://openreview.net/forum?id=x7usmidzxj | Yusu Hong,Junhong Lin | NIPS 2024,Poster | In this paper, we study Adam in non-convex smooth scenarios with potential unbounded gradients and affine variance noise. We consider a general noise model which governs affine variance noise, bounded noise, and sub-Gaussian noise. We show that Adam with a specific hyper-parameter setup can find a stationary point with a $\mathcal{O}(\text{poly}(\log T)/\sqrt{T})$ rate in high probability under this general noise model where $T$ denotes total number iterations, matching the lower rate of stochastic first-order algorithms up to logarithm factors. We also provide a probabilistic convergence result for Adam under a generalized smooth condition which allows unbounded smoothness parameters and has been illustrated empirically to capture the smooth property of many practical objective functions more accurately. | https://openreview.net/pdf/53b61c417a7761bfb6f0d648f3d93f54a1153174.pdf |
Ad Auctions for LLMs via Retrieval Augmented Generation | https://openreview.net/forum?id=Ujo8V7iXmR | https://openreview.net/forum?id=Ujo8V7iXmR | MohammadTaghi Hajiaghayi,Sebastien Lahaie,Keivan Rezaei,Suho Shin | NIPS 2024,Poster | In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity. This paper introduces novel auction mechanisms for ad allocation and pricing within the textual outputs of LLMs, leveraging retrieval-augmented generation (RAG). We propose a \emph{segment auction} where an ad is probabilistically retrieved for each discourse segment (paragraph, section, or entire output) according to its bid and relevance, following the RAG framework, and priced according to competing bids. We show that our auction maximizes logarithmic social welfare, a new notion of welfare that balances allocation efficiency and fairness, and we characterize the associated incentive-compatible pricing rule. These results are extended to multi-ad allocation per segment. An empirical evaluation validates the feasibility and effectiveness of our approach over several ad auction scenarios, and exhibits inherent tradeoffs in metrics as we allow the LLM more flexibility to allocate ads. | https://openreview.net/pdf/1a43bddd10e8ed2f5ca0b3de382ea2aca7da548b.pdf |
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models | https://openreview.net/forum?id=VzOgnDJMgh | https://openreview.net/forum?id=VzOgnDJMgh | Jinghan Jia,Jiancheng Liu,Yihua Zhang,Parikshit Ram,Nathalie Baracaldo,Sijia Liu | NIPS 2024,Poster | The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. LLM unlearning is designed to reduce the impact of undesirable data influences and associated model capabilities without diminishing the utility of the model if unrelated to the information being forgotten. Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined. In this paper, we systematically explore how model weights interact with unlearning processes in LLMs and we design the weight attribution-guided LLM unlearning method, WAGLE, which unveils the interconnections between 'influence' of weights and 'influence' of data to forget and retain in LLM generation. By strategically guiding the LLM unlearning across different types of unlearning methods and tasks, WAGLE can erase the undesired content, while maintaining the performance of the original tasks. We refer to the weight attribution-guided LLM unlearning method as WAGLE, which unveils the interconnections between 'influence' of weights and 'influence' of data to forget and retain in LLM generation. Our extensive experiments show that WAGLE boosts unlearning performance across a range of LLM unlearning methods such as gradient difference and (negative) preference optimization, applications such as fictitious unlearning (TOFU benchmark), malicious use prevention (WMDP benchmark), and copyrighted information removal, and models including Zephyr-7b-beta and Llama2-7b. To the best of our knowledge, our work offers the first principled method for attributing and pinpointing the influential weights in enhancing LLM unlearning. It stands in contrast to previous methods that lack weight attribution and simpler weight attribution techniques. | https://openreview.net/pdf/f8e6edd72761c9672749d9c628233d2df16aae08.pdf |
Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation | https://openreview.net/forum?id=PXGY9Fz8vC | https://openreview.net/forum?id=PXGY9Fz8vC | Trenton Chang,Lindsay Warrenburg,Sae-Hwan Park,Ravi B Parikh,Maggie Makar,Jenna Wiens | NIPS 2024,Poster | In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or *agents,* may *game* model decisions by manipulating their inputs to the model to obtain better outcomes and maximize some utility. We consider a multi-agent setting where the goal is to identify the “worst offenders:” agents that are gaming most aggressively. However, identifying such agents is difficult without knowledge of their utility function. Thus, we introduce a framework in which each agent’s tendency to game is parameterized via a scalar. We show that this gaming parameter is only partially identifiable. By recasting the problem as a causal effect estimation problem where different agents represent different “treatments,” we prove that a ranking of all agents by their gaming parameters is identifiable. We present empirical results in a synthetic data study validating the usage of causal effect estimation for gaming detection and show in a case study of diagnosis coding behavior in the U.S. that our approach highlights features associated with gaming. | https://openreview.net/pdf/7f31354db4587aad2bba879b475c0a1ac5a5c57e.pdf |
Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process | https://openreview.net/forum?id=psG4LXlDNs | https://openreview.net/forum?id=psG4LXlDNs | Jiashuo Jiang,Yinyu Ye | NIPS 2024,Poster | We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making. In this problem, we are given finite resources and a MDP with unknown transition probabilities. At each stage, we take an action, collecting a reward and consuming some resources, all assumed to be unknown and need to be learned over time. In this work, we take the first step towards deriving optimal problem-dependent guarantees for the CMDP problems. We derive a logarithmic regret bound, which translates into a $O(\frac{1}{\Delta\cdot\epsilon}\cdot\log^2(1/\epsilon))$ sample complexity bound, with $\Delta$ being a problem-dependent parameter, yet independent of $\epsilon$. Our sample complexity bound improves upon the state-of-art $O(1/\epsilon^2)$ sample complexity for CMDP problems established in the previous literature, in terms of the dependency on $\epsilon$. To achieve this advance, we develop a new framework for analyzing CMDP problems. To be specific, our algorithm operates in the primal space and we resolve the primal LP for the CMDP problem at each period in an online manner, with \textit{adaptive} remaining resource capacities. The key elements of our algorithm are: i) a characterization of the instance hardness via LP basis, ii) an eliminating procedure that identifies one optimal basis of the primal LP, and; iii) a resolving procedure that is adaptive to the remaining resources and sticks to the characterized optimal basis. | https://openreview.net/pdf/d266944c7c83f38bc65d9643812af49872f309c1.pdf |
Scaling Laws in Linear Regression: Compute, Parameters, and Data | https://openreview.net/forum?id=PH7sdEanXP | https://openreview.net/forum?id=PH7sdEanXP | Licong Lin,Jingfeng Wu,Sham M. Kakade,Peter Bartlett,Jason D. Lee | NIPS 2024,Poster | Empirically, large-scale deep learning models often satisfy a neural scaling law: the test error of the trained model improves polynomially as the model size and data size grow. However, conventional wisdom suggests the test error consists of approximation, bias, and variance errors, where the variance error increases with model size. This disagrees with the general form of neural scaling laws, which predict that increasing model size monotonically improves performance.
We study the theory of scaling laws in an infinite dimensional linear regression setup. Specifically, we consider a model with $M$ parameters as a linear function of sketched covariates. The model is trained by one-pass stochastic gradient descent (SGD) using $N$ data. Assuming the optimal parameter satisfies a Gaussian prior and the data covariance matrix has a power-law spectrum of degree $a>1$, we show that the reducible part of the test error is $\Theta(M^{-(a-1)} + N^{-(a-1)/a})$. The variance error, which increases with $M$, is dominated by the other errors due to the implicit regularization of SGD, thus disappearing from the bound. Our theory is consistent with the empirical neural scaling laws and verified by numerical simulation. | https://openreview.net/pdf/0f2b588586b8f9357aca924f3353b0c13b102112.pdf |
Learning Image Priors Through Patch-Based Diffusion Models for Solving Inverse Problems | https://openreview.net/forum?id=HGnxhHz6ss | https://openreview.net/forum?id=HGnxhHz6ss | Jason Hu,Bowen Song,Xiaojian Xu,Liyue Shen,Jeffrey A Fessler | NIPS 2024,Poster | Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems,
but the training process is computationally expensive and requires lots of data.
Such bottlenecks prevent most existing works from being feasible for high-dimensional and high-resolution data such as 3D images.
This paper proposes a method to learn an efficient data prior for the entire image by training diffusion models only on patches of images.
Specifically, we propose a patch-based position-aware diffusion inverse solver, called PaDIS, where we obtain the score function of the whole image through scores of patches and their positional encoding and utilize this as the prior for solving inverse problems.
First of all, we show that this diffusion model achieves an improved memory efficiency and data efficiency
while still maintaining the capability to generate entire images via positional encoding.
Additionally, the proposed PaDIS model is highly flexible and can be plugged in with different diffusion inverse solvers (DIS).
We demonstrate that the proposed PaDIS approach enables solving various inverse problems in both natural and medical image domains, including CT reconstruction, deblurring, and superresolution, given only patch-based priors.
Notably, PaDIS outperforms previous DIS methods trained on entire image priors in the case of limited training data, demonstrating the data efficiency of our proposed approach by learning patch-based prior. | https://openreview.net/pdf/5c1849cec489253b53dd5ced49cd88613b54d884.pdf |
Amortized Fourier Neural Operators | https://openreview.net/forum?id=a6em980M9x | https://openreview.net/forum?id=a6em980M9x | Zipeng Xiao,Siqi Kou,Zhongkai Hao,Bokai Lin,Zhijie Deng | NIPS 2024,Poster | Fourier Neural Operators (FNOs) have shown promise for solving partial differential equations (PDEs).
Typically, FNOs employ separate parameters for different frequency modes to specify tunable kernel integrals in Fourier space, which, yet, results in an undesirably large number of parameters when solving high-dimensional PDEs.
A workaround is to abandon the frequency modes exceeding a predefined threshold, but this limits the FNOs' ability to represent high-frequency details and poses non-trivial challenges for hyper-parameter specification.
To address these, we propose AMortized Fourier Neural Operator (AM-FNO), where an amortized neural parameterization of the kernel function is deployed to accommodate arbitrarily many frequency modes using a fixed number of parameters.
We introduce two implementations of AM-FNO, based on the recently developed, appealing Kolmogorov–Arnold Network (KAN) and Multi-Layer Perceptrons (MLPs) equipped with orthogonal embedding functions respectively.
We extensively evaluate our method on diverse datasets from various domains and observe up to 31\% average improvement compared to competing neural operator baselines. | https://openreview.net/pdf/ac3e9bb4adc6f5e7eda9fb232b311cc5daf2ded2.pdf |
Retrieval-Augmented Diffusion Models for Time Series Forecasting | https://openreview.net/forum?id=dRJJt0Ji48 | https://openreview.net/forum?id=dRJJt0Ji48 | Jingwei Liu,Ling Yang,Hongyan Li,Shenda Hong | NIPS 2024,Poster | While time series diffusion models have received considerable focus from many recent works, the performance of existing models remains highly unstable. Factors limiting time series diffusion models include insufficient time series datasets and the absence of guidance. To address these limitations, we propose a Retrieval-Augmented Time series Diffusion model (RATD). The framework of RATD consists of two parts: an embedding-based retrieval process and a reference-guided diffusion model. In the first part, RATD retrieves the time series that are most relevant to historical time series from the database as references. The references are utilized to guide the denoising process in the second part. Our approach allows leveraging meaningful samples within the database to aid in sampling, thus maximizing the utilization of datasets. Meanwhile, this reference-guided mechanism also compensates for the deficiencies of existing time series diffusion models in terms of guidance. Experiments and visualizations on multiple datasets demonstrate the effectiveness of our approach, particularly in complicated prediction tasks. Our code is available at https://github.com/stanliu96/RATD | https://openreview.net/pdf/e87ce496bc882c66ee7f20b01c0a67af85c06f6f.pdf |
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems | https://openreview.net/forum?id=VR2RdSxtzs | https://openreview.net/forum?id=VR2RdSxtzs | Bin Lei,Yi Zhang,Shan Zuo,Ali Payani,Caiwen Ding | NIPS 2024,Poster | Recent advancements in large language models, such as GPT-4, have demonstrated remarkable capabilities in processing standard queries. Despite these advancements, their performance substantially declines in advanced mathematical problems requiring complex, multi-step logical reasoning. To enhance their inferential capabilities, current research has delved into prompting engineering, exemplified by methodologies such as the Tree of Thought and Graph of Thought.
Nonetheless, these existing approaches encounter two significant limitations. Firstly, their effectiveness in tackling complex mathematical problems is somewhat constrained. Secondly, the necessity to design distinct prompts for individual problems hampers their generalizability.
In response to these limitations, this paper introduces the Multi-Agent System for conditional Mining (MACM) prompting method. It not only resolves intricate mathematical problems but also demonstrates strong generalization capabilities across various mathematical contexts.
With the assistance of MACM, the accuracy of GPT-4 Turbo on the most challenging level five mathematical problems in the MATH dataset increase from $\mathbf{54.68\\%} \text{ to } \mathbf{76.73\\%}$. | https://openreview.net/pdf/361f70e303eefe87ee3e42f46fb2d3d21347df37.pdf |
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models | https://openreview.net/forum?id=pRQmRaonxf | https://openreview.net/forum?id=pRQmRaonxf | Chengshuai Shi,Kun Yang,Jing Yang,Cong Shen | NIPS 2024,Poster | The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years. While theoretical understanding has been obtained for ICL in reinforcement learning (RL), the previous results are largely confined to the single-agent setting. This work proposes to further explore the in-context learning capabilities of pre-trained transformer models in competitive multi-agent games, i.e., in-context game-playing (ICGP). Focusing on the classical two-player zero-sum games, theoretical guarantees are provided to demonstrate that pre-trained transformers can provably learn to approximate Nash equilibrium in an in-context manner for both decentralized and centralized learning settings. As a key part of the proof, constructional results are established to demonstrate that the transformer architecture is sufficiently rich to realize celebrated multi-agent game-playing algorithms, in particular, decentralized V-learning and centralized VI-ULCB. | https://openreview.net/pdf/a739b11c92fd5cfc39cc60917a0bafb7c9f5b8cf.pdf |
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration | https://openreview.net/forum?id=O0nBMRlkc8 | https://openreview.net/forum?id=O0nBMRlkc8 | Junyang Wang,Haiyang Xu,Haitao Jia,Xi Zhang,Ming Yan,Weizhou Shen,Ji Zhang,Fei Huang,Jitao Sang | NIPS 2024,Poster | Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation assistants. Instead, MLLM-based agents, which enhance capabilities through tool invocation, are gradually being applied to this scenario. However, the two major navigation challenges in mobile device operation tasks — task progress navigation and focus content navigation — are difficult to effectively solve under the single-agent architecture of existing work. This is due to the overly long token sequences and the interleaved text-image data format, which limit performance. To address these navigation challenges effectively, we propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. The planning agent condenses lengthy, interleaved image-text history operations and screens summaries into a pure-text task progress, which is then passed on to the decision agent. This reduction in context length makes it easier for decision agent to navigate the task progress. To retain focus content, we design a memory unit that updates with task progress by decision agent. Additionally, to correct erroneous operations, the reflection agent observes the outcomes of each operation and handles any mistake accordingly. Experimental results indicate that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture of Mobile-Agent. The code is open-sourced at https://github.com/X-PLUG/MobileAgent. | https://openreview.net/pdf/1884d55b0eac95c14035e897dcbb1c8186bcd65e.pdf |
MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction | https://openreview.net/forum?id=muYhNDlxWc | https://openreview.net/forum?id=muYhNDlxWc | Jiahe Chen,Jinkun Cao,Dahua Lin,Kris M. Kitani,Jiangmiao Pang | NIPS 2024,Poster | To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity.
The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and supervision losses.
Instead, we propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction.
The prior is constructed by analyzing the trajectory patterns in the training samples without requiring extra annotations while showing better expressiveness and being multi-modal and asymmetric.
Besides diversity, it also provides better controllability for probabilistic trajectory generation.
We name our method Mixed Gaussian Flow (MGF). It achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets. Code is available at https://github.com/mulplue/MGF. | https://openreview.net/pdf/a24b2249847a2068a01c6fa992db6a0aad0d0e19.pdf |
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation | https://openreview.net/forum?id=RrTjcbcHEH | https://openreview.net/forum?id=RrTjcbcHEH | István Sárándi,Gerard Pons-Moll | NIPS 2024,Poster | With the explosive growth of available training data, single-image 3D human modeling is ahead of a transition to a data-centric paradigm.
A key to successfully exploiting data scale is to design flexible models that can be supervised from various heterogeneous data sources produced by different researchers or vendors.
To this end, we propose a simple yet powerful paradigm for seamlessly unifying different human pose and shape-related tasks and datasets.
Our formulation is centered on the ability - both at training and test time - to query any arbitrary point of the human volume, and obtain its estimated location in 3D.
We achieve this by learning a continuous neural field of body point localizer functions, each of which is a differently parameterized 3D heatmap-based convolutional point localizer (detector).
For generating parametric output, we propose an efficient post-processing step for fitting SMPL-family body models to nonparametric joint and vertex predictions.
With this approach, we can naturally exploit differently annotated data sources including mesh, 2D/3D skeleton and dense pose, without having to convert between them, and thereby train large-scale 3D human mesh and skeleton estimation models that outperform the state-of-the-art on several public benchmarks including 3DPW, EMDB, EHF, SSP-3D and AGORA by a considerable margin.
We release our code and models to foster downstream research. | https://openreview.net/pdf/bb9be9482b7a972c9ff1a24f3d75ee22d6195fdd.pdf |
Efficient Prompt Optimization Through the Lens of Best Arm Identification | https://openreview.net/forum?id=FLNnlfBGMo | https://openreview.net/forum?id=FLNnlfBGMo | Chengshuai Shi,Kun Yang,Zihan Chen,Jundong Li,Jing Yang,Cong Shen | NIPS 2024,Poster | The remarkable instruction-following capability of large language models (LLMs) has sparked a growing interest in automatically finding good prompts, i.e., prompt optimization. Most existing works follow the scheme of selecting from a pre-generated pool of candidate prompts. However, these designs mainly focus on the generation strategy, while limited attention has been paid to the selection method. Especially, the cost incurred during the selection (e.g., accessing LLM and evaluating the responses) is rarely explicitly considered. To overcome this limitation, this work provides a principled framework, TRIPLE, to efficiently perform prompt selection under an explicit budget constraint. TRIPLE is built on a novel connection established between prompt optimization and fixed-budget best arm identification (BAI-FB) in multi-armed bandits (MAB); thus, it is capable of leveraging the rich toolbox from BAI-FB systematically and also incorporating unique characteristics of prompt optimization. Extensive experiments on multiple well-adopted tasks using various LLMs demonstrate the remarkable performance improvement of TRIPLE over baselines while satisfying the limited budget constraints. As an extension, variants of TRIPLE are proposed to efficiently select examples for few-shot prompts, also achieving superior empirical performance. | https://openreview.net/pdf/d55bf9078917118b9c52834d084c1245727ed3e9.pdf |
Fast Best-of-N Decoding via Speculative Rejection | https://openreview.net/forum?id=348hfcprUs | https://openreview.net/forum?id=348hfcprUs | Hanshi Sun,Momin Haider,Ruiqi Zhang,Huitao Yang,Jiahao Qiu,Ming Yin,Mengdi Wang,Peter Bartlett,Andrea Zanette | NIPS 2024,Poster | The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during a phase called post-training. While predominant, these post-training methods add substantial complexity before LLMs can be deployed. Inference-time alignment methods avoid the complex post-training step and instead bias the generation towards responses that are aligned with human preferences. The best-known inference-time alignment method, called Best-of-N, is as effective as the state-of-the-art post-training procedures. Unfortunately, Best-of-N requires vastly more resources at inference time than standard decoding strategies, which makes it computationally not viable. In this work, we introduce Speculative Rejection, a computationally-viable inference-time alignment algorithm. It generates high-scoring responses according to a given reward model, like Best-of-N does, while being between 16 to 32 times more computationally efficient. | https://openreview.net/pdf/1185ba27284299162dd748d2582af8def317545b.pdf |
Full-Atom Peptide Design with Geometric Latent Diffusion | https://openreview.net/forum?id=IAQNJUJe8q | https://openreview.net/forum?id=IAQNJUJe8q | Xiangzhe Kong,Yinjun Jia,Wenbing Huang,Yang Liu | NIPS 2024,Poster | Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom Peptide design with Geometric LAtent Diffusion (PepGLAD) given the binding site. We first establish a benchmark consisting of both 1D sequences and 3D structures from Protein Data Bank (PDB) and literature for systematic evaluation. We then identify two major challenges of leveraging current diffusion-based models for peptide design: the full-atom geometry and the variable binding geometry. To tackle the first challenge, PepGLAD derives a variational autoencoder that first encodes full-atom residues of variable size into fixed-dimensional latent representations, and then decodes back to the residue space after conducting the diffusion process in the latent space. For the second issue, PepGLAD explores a receptor-specific affine transformation to convert the 3D coordinates into a shared standard space, enabling better generalization ability across different binding shapes. Experimental Results show that our method not only improves diversity and binding affinity significantly in the task of sequence-structure co-design, but also excels at recovering reference structures for binding conformation generation. | https://openreview.net/pdf/69729ae7bb5ba90164d10c5cefa3f252d78a5c65.pdf |
3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning | https://openreview.net/forum?id=IVqzbuLfoL | https://openreview.net/forum?id=IVqzbuLfoL | Zhifan Ye,Chenxi Wan,Chaojian Li,Jihoon Hong,Sixu Li,Leshu Li,Yongan Zhang,Yingyan Celine Lin | NIPS 2024,Poster | 3D Gaussian splatting has recently emerged as a promising technique for novel view synthesis from sparse image sets, yet comes at the cost of requiring millions of 3D Gaussian primitives to reconstruct each 3D scene. This largely limits its application to resource-constrained devices and applications.
Despite advances in Gaussian pruning techniques that aim to remove individual 3D Gaussian primitives, the significant reduction in primitives often fails to translate into commensurate increases in rendering speed, impeding efficiency and practical deployment. We identify that this discrepancy arises due to the overlooked impact of fragment count per Gaussian (i.e., the number of pixels each Gaussian is projected onto). To bridge this gap and meet the growing demands for efficient on-device 3D Gaussian rendering, we propose fragment pruning, an orthogonal enhancement to existing pruning methods that can significantly accelerate rendering by selectively pruning fragments within each Gaussian. Our pruning framework dynamically optimizes the pruning threshold for each Gaussian, markedly improving rendering speed and quality. Extensive experiments in both static and dynamic scenes validate the effectiveness of our approach. For instance, by integrating our fragment pruning technique with state-of-the-art Gaussian pruning methods, we achieve up to a 1.71$\times$ speedup on an edge GPU device, the Jetson Orin NX, and enhance rendering quality by an average of 0.16 PSNR on the Tanks\&Temples dataset. Our code is available at https://github.com/GATECH-EIC/Fragment-Pruning. | https://openreview.net/pdf/0f4648bcb47f776c689bfd3c5d96e9f9131c9021.pdf |
Dimension-free Private Mean Estimation for Anisotropic Distributions | https://openreview.net/forum?id=kRwQCAIA7z | https://openreview.net/forum?id=kRwQCAIA7z | Yuval Dagan,Michael Jordan,Xuelin Yang,Lydia Zakynthinou,Nikita Zhivotovskiy | NIPS 2024,Poster | We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over $\mathbb{R}^d$ suffer from a curse of dimensionality, as they require $\Omega(d^{1/2})$ samples to achieve non-trivial error, even in cases where $O(1)$ samples suffice without privacy. This rate is unavoidable when the distribution is isotropic, namely, when the covariance is a multiple of the identity matrix. Yet, real-world data is often highly anisotropic, with signals concentrated on a small number of principal components. We develop estimators that are appropriate for such signals---our estimators are $(\varepsilon,\delta)$-differentially private and have sample complexity that is dimension-independent for anisotropic subgaussian distributions. Given $n$ samples from a distribution with known covariance-proxy $\Sigma$ and unknown mean $\mu$, we present an estimator $\hat{\mu}$ that achieves error, $\|\hat{\mu}-\mu\|_2\leq \alpha$, as long as $n\gtrsim \text{tr}(\Sigma)/\alpha^2+ \text{tr}(\Sigma^{1/2})/(\alpha\varepsilon)$. We show that this is the optimal sample complexity for this task up to logarithmic factors. Moreover, for the case of unknown covariance, we present an algorithm whose sample complexity has improved dependence on the dimension, from $d^{1/2}$ to $d^{1/4}$. | https://openreview.net/pdf/90919b8b60143f0e171dd5310efcb65e20de7354.pdf |
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents | https://openreview.net/forum?id=Nf4MHF1pi5 | https://openreview.net/forum?id=Nf4MHF1pi5 | Wenkai Yang,Xiaohan Bi,Yankai Lin,Sishuo Chen,Jie Zhou,Xu Sun | NIPS 2024,Poster | Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based agents during applications. However, the safety issues of LLM-based agents are currently under-explored. In this work, we take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis of different forms of agent backdoor attacks. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms: (1) From the perspective of the final attacking outcomes, the agent backdoor attacker can not only choose to manipulate the final output distribution, but also introduce the malicious behavior in an intermediate reasoning step only, while keeping the final output correct. (2) Furthermore, the former category can be divided into two subcategories based on trigger locations, in which the backdoor trigger can either be hidden in the user query or appear in an intermediate observation returned by the external environment. We implement the above variations of agent backdoor attacks on two typical agent tasks including web shopping and tool utilization. Extensive experiments show that LLM-based agents suffer severely from backdoor attacks and such backdoor vulnerability cannot be easily mitigated by current textual backdoor defense algorithms. This indicates an urgent need for further research on the development of targeted defenses against backdoor attacks on LLM-based agents. Warning: This paper may contain biased content. | https://openreview.net/pdf/14dda2e2e067d5c1fc5179293fd0d4072276f210.pdf |
Beyond Accuracy: Tracking more like Human via Visual Search | https://openreview.net/forum?id=LezAEImfoc | https://openreview.net/forum?id=LezAEImfoc | Dailing Zhang,Shiyu Hu,Xiaokun Feng,Xuchen Li,Meiqi Wu,Jing Zhang,Kaiqi Huang | NIPS 2024,Poster | Human visual search ability enables efficient and accurate tracking of an arbitrary moving target, which is a significant research interest in cognitive neuroscience. The recently proposed Central-Peripheral Dichotomy (CPD) theory sheds light on how humans effectively process visual information and track moving targets in complex environments. However, existing visual object tracking algorithms still fall short of matching human performance in maintaining tracking over time, particularly in complex scenarios requiring robust visual search skills. These scenarios often involve Spatio-Temporal Discontinuities (i.e., STDChallenge), prevalent in long-term tracking and global instance tracking. To address this issue, we conduct research from a human-like modeling perspective: (1) Inspired by the CPD, we pro- pose a new tracker named CPDTrack to achieve human-like visual search ability. The central vision of CPDTrack leverages the spatio-temporal continuity of videos to introduce priors and enhance localization precision, while the peripheral vision improves global awareness and detects object movements. (2) To further evaluate and analyze STDChallenge, we create the STDChallenge Benchmark. Besides, by incorporating human subjects, we establish a human baseline, creating a high- quality environment specifically designed to assess trackers’ visual search abilities in videos across STDChallenge. (3) Our extensive experiments demonstrate that the proposed CPDTrack not only achieves state-of-the-art (SOTA) performance in this challenge but also narrows the behavioral differences with humans. Additionally, CPDTrack exhibits strong generalizability across various challenging benchmarks. In summary, our research underscores the importance of human-like modeling and offers strategic insights for advancing intelligent visual target tracking. Code and models are available at https://github.com/ZhangDailing8/CPDTrack. | https://openreview.net/pdf/11960c0cf6a34cdc6a956f476a0fb526022a4514.pdf |
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms | https://openreview.net/forum?id=8W5ADJOKcv | https://openreview.net/forum?id=8W5ADJOKcv | Chengyuan Deng,Jie Gao,Kevin Lu,Feng Luo,Hongbin Sun,Cheng Xin | NIPS 2024,Poster | We introduce \textbf{N}on-\textbf{Euc}lidean-\textbf{MDS} (Neuc-MDS), which extends Multidimensional Scaling (MDS) to generate outputs that can be non-Euclidean and non-metric. The main idea is to generalize the inner product to other symmetric bilinear forms to utilize the negative eigenvalues of dissimiliarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS, the sum of squared pairwise error. We provide an in-depth error analysis and proofs of the optimality in minimizing lower bounds of STRESS. We demonstrate Neuc-MDS's ability to address limitations of classical MDS raised by prior research, and test it on various synthetic and real-world datasets in comparison with both linear and non-linear dimension reduction methods. | https://openreview.net/pdf/39cf858fefc937ab29191f4ab0dc60436e3a517a.pdf |
Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? | https://openreview.net/forum?id=4lGPSbGe11 | https://openreview.net/forum?id=4lGPSbGe11 | Garud Iyengar,Henry Lam,Tianyu Wang | NIPS 2024,Poster | Cross-Validation (CV) is the default choice for estimate the out-of-sample performance of machine learning models. Despite its wide usage, their statistical benefits have remained half-understood, especially in challenging nonparametric regimes. In this paper we fill in this gap and show that, in terms of estimating the out-of-sample performances, for a wide spectrum of models, CV does not statistically outperform the simple ``plug-in'' approach where one reuses training data for testing evaluation. Specifically, in terms of both the asymptotic bias and coverage accuracy of the associated interval for out-of-sample evaluation, $K$-fold CV provably cannot outperform plug-in regardless of the rate at which the parametric or nonparametric models converge. Leave-one-out CV can have a smaller bias as compared to plug-in; however, this bias improvement is negligible compared to the variability of the evaluation, and in some important cases leave-one-out again does not outperform plug-in once this variability is taken into account. We obtain our theoretical comparisons via a novel higher-order Taylor analysis that dissects the limit theorems of testing evaluations, which applies to model classes that are not amenable to previously known sufficient conditions. Our numerical results demonstrate that plug-in performs indeed no worse than CV in estimating model performance across a wide range of examples. | https://openreview.net/pdf/189b1a44f32cdb8dc7985f8314688df2d9804e5f.pdf |
Target-Guided Adversarial Point Cloud Transformer Towards Recognition Against Real-world Corruptions | https://openreview.net/forum?id=FcUyz33OED | https://openreview.net/forum?id=FcUyz33OED | Jie Wang,Tingfa Xu,Lihe Ding,Jianan Li | NIPS 2024,Poster | Achieving robust 3D perception in the face of corrupted data presents an challenging hurdle within 3D vision research. Contemporary transformer-based point cloud recognition models, albeit advanced, tend to overfit to specific patterns, consequently undermining their robustness against corruption. In this work, we introduce the Target-Guided Adversarial Point Cloud Transformer, termed APCT, a novel architecture designed to augment global structure capture through an adversarial feature erasing mechanism predicated on patterns discerned at each step during training. Specifically, APCT integrates an Adversarial Significance Identifier and a Target-guided Promptor. The Adversarial Significance Identifier, is tasked with discerning token significance by integrating global contextual analysis, utilizing a structural salience index algorithm alongside an auxiliary supervisory mechanism. The Target-guided Promptor, is responsible for accentuating the propensity for token discard within the self-attention mechanism, utilizing the value derived above, consequently directing the model attention towards alternative segments in subsequent stages. By iteratively applying this strategy in multiple steps during training, the network progressively identifies and integrates an expanded array of object-associated patterns. Extensive experiments demonstrate that our method achieves state-of-the-art results on multiple corruption benchmarks. | https://openreview.net/pdf/39d342b992430643e7c7bb388857230d156519e2.pdf |
Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints | https://openreview.net/forum?id=pG380vLYRU | https://openreview.net/forum?id=pG380vLYRU | Zhenwei Lin,Qi Deng | NIPS 2024,Poster | In this paper, we introduce faster accelerated primal-dual algorithms for minimizing a convex function subject to strongly convex function constraints.
Prior to our work, the best complexity bound was $\mathcal{O}(1/{\varepsilon})$, regardless of the strong convexity of the constraint function.
It is unclear whether the strong convexity assumption can enable even better convergence results.
To address this issue, we have developed novel techniques to progressively estimate the strong convexity of the Lagrangian function.
Our approach, for the first time, effectively leverages the constraint strong convexity, obtaining an improved complexity of $\mathcal{O}(1/\sqrt{\varepsilon})$. This rate matches the complexity lower bound for strongly-convex-concave saddle point optimization and is therefore order-optimal.
We show the superior performance of our methods in sparsity-inducing constrained optimization, notably Google's personalized PageRank problem. Furthermore, we show that a restarted version of the proposed methods can effectively identify the optimal solution's sparsity pattern within a finite number of steps, a result that appears to have independent significance. | https://openreview.net/pdf/39d2a679b231a9c79f5e0031e24c97e052f8d1b3.pdf |
Exactly Minimax-Optimal Locally Differentially Private Sampling | https://openreview.net/forum?id=Dr7UarlhVE | https://openreview.net/forum?id=Dr7UarlhVE | Hyun-Young Park,Shahab Asoodeh,Si-Hyeon Lee | NIPS 2024,Poster | The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the $f$-divergence between original and sampling distributions as the utility measure. We characterize the exact PUT for both finite and continuous data spaces under some mild conditions on the data distributions, and propose sampling mechanisms that are universally optimal for all $f$-divergences. Our numerical experiments demonstrate the superiority of our mechanisms over baselines, in terms of theoretical utilities for finite data space and of empirical utilities for continuous data space. | https://openreview.net/pdf/4db55857d23f0b9e879bd411410654e42341a38f.pdf |
Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation | https://openreview.net/forum?id=x4Kk4FxLs3 | https://openreview.net/forum?id=x4Kk4FxLs3 | Lingxiao Zhao,Xueying Ding,Leman Akoglu | NIPS 2024,Poster | Graph generation has been dominated by autoregressive models due to their simplicity and effectiveness, despite their sensitivity to ordering. Yet diffusion models have garnered increasing attention, as they offer comparable performance while being permutation-invariant. Current graph diffusion models generate graphs in a one-shot fashion, but they require extra features and thousands of denoising steps to achieve optimal performance. We introduce PARD, a Permutation-invariant Auto Regressive Diffusion model that integrates diffusion models with autoregressive methods. PARD harnesses the effectiveness and efficiency of the autoregressive model while maintaining permutation invariance without ordering sensitivity. Specifically, we show that contrary to sets, elements in a graph are not entirely un-ordered and there is a unique partial order for nodes and edges. With this partial order, PARD generates a graph in a block-by-block, autoregressive fashion, where each block’s probability is conditionally modeled by a shared diffusion model with an equivariant network. To ensure efficiency while being expressive, we further propose a higher-order graph transformer, which integrates transformer with PPGN (Maronet al., 2019). Like GPT, we extend the higher-order graph transformer to support parallel training of all blocks. Without any extra features, PARD achieves state-of-the-art performance on molecular and non-molecular datasets, and scales to large datasets like MOSES containing 1.9M molecules. | https://openreview.net/pdf/c1a62f1c53db519f90d14aed3ca68d4ed80a9146.pdf |
Robust Reinforcement Learning with General Utility | https://openreview.net/forum?id=8Uyfr5TcNR | https://openreview.net/forum?id=8Uyfr5TcNR | Ziyi Chen,Yan Wen,Zhengmian Hu,Heng Huang | NIPS 2024,Poster | Reinforcement Learning (RL) problem with general utility is a powerful decision making framework that covers standard RL with cumulative cost, exploration problems, and demonstration learning. Existing works on RL with general utility do not consider the robustness under environmental perturbation, which is important to adapt RL system in the real-world environment that differs from the training environment. To train a robust policy, we propose a robust RL framework with general utility, which subsumes many existing RL frameworks including RL, robust RL, RL with general utility, constrained RL, robust constrained RL, pure exploration, robust entropy regularized RL, etc. Then we focus on popular convex utility functions, with which our proposed learning framework is a challenging nonconvex-nonconcave minimax optimization problem, and design a two-phase stochastic policy gradient type algorithm and obtain its sample complexity result for gradient convergence. Furthermore, for convex utility on a widely used polyhedral ambiguity set, we design an algorithm and obtain its convergence rate to a global optimal solution. | https://openreview.net/pdf/bd1e309302d23d5c27fb998972d826f00ff8c3cc.pdf |
Online Estimation via Offline Estimation: An Information-Theoretic Framework | https://openreview.net/forum?id=sks7x4I8Bh | https://openreview.net/forum?id=sks7x4I8Bh | Dylan J Foster,Yanjun Han,Jian Qian,Alexander Rakhlin | NIPS 2024,Poster | The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design (''offline estimation''), while the contemporary theory of online learning provides algorithms for estimation under adaptively chosen covariates (''online estimation''). Motivated by connections between estimation and interactive decision making, we ask: is it possible to convert offline estimation algorithms into online estimation algorithms in a black-box fashion? We investigate this question from an information-theoretic perspective by introducing a new framework, Oracle-Efficient Online Estimation (OEOE), where the learner can only interact with the data stream indirectly through a sequence of offline estimators produced by a black-box algorithm operating on the stream. Our main results settle the statistical and computational complexity of online estimation in this framework.
$\bullet$ Statistical complexity. We show that information-theoretically, there exist algorithms that achieve near-optimal online estimation error via black-box offline estimation oracles, and give a nearly-tight characterization for minimax rates in the OEOE framework.
$\bullet$ Computational complexity. We show that the guarantees above cannot be achieved in a computationally efficient fashion in general, but give a refined characterization for the special case of conditional density estimation: computationally efficient online estimation via black-box offline estimation is possible whenever it is possible via unrestricted algorithms.
Finally, we apply our results to give offline oracle-efficient algorithms for interactive decision making. | https://openreview.net/pdf/b8cfbc277ea416f34c48378cb8a72149176fc155.pdf |
Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models | https://openreview.net/forum?id=G0v0TxX01N | https://openreview.net/forum?id=G0v0TxX01N | Jiacheng Ye,Shansan Gong,Liheng Chen,Lin Zheng,Jiahui Gao,Han Shi,Chuan Wu,Xin Jiang,Zhenguo Li,Wei Bi,Lingpeng Kong | NIPS 2024,Poster | Recently, diffusion models have garnered significant interest in the field of text processing due to their many potential advantages compared to conventional autoregressive models.
In this work, we propose Diffusion-of-Thought (DoT), a novel approach that integrates diffusion models with Chain-of-Thought, a well-established technique for improving the reasoning ability of autoregressive language models. In contrast to autoregressive language models that make decisions in a left-to-right, token-by-token manner, DoT allows reasoning steps to diffuse over time through a diffusion language model and offers greater flexibility in trading-off computation for reasoning performance. Our experimental results demonstrate the effectiveness of DoT in multi-digit multiplication, boolean logic, and grade school math problems. In addition to that, DoT showcases promising self-correction abilities and benefits from existing reasoning-enhancing techniques like self-consistency decoding. Our findings contribute to the understanding and development of reasoning with diffusion language models. | https://openreview.net/pdf/c87cdf6b6e90f2c3f736be50639670dba4245f12.pdf |
No-Regret Bandit Exploration based on Soft Tree Ensemble Model | https://openreview.net/forum?id=cKKXBhyijL | https://openreview.net/forum?id=cKKXBhyijL | Shogo Iwazaki,Shinya Suzumura | NIPS 2024,Poster | We propose a novel stochastic bandit algorithm that employs reward estimates using a tree ensemble model. Specifically, our focus is on a soft tree model, a variant of the conventional decision tree that has undergone both practical and theoretical scrutiny in recent years. By deriving several non-trivial properties of soft trees, we extend the existing analytical techniques used for neural bandit algorithms to our soft tree-based algorithm. We demonstrate that our algorithm achieves a smaller cumulative regret compared to the existing ReLU-based neural bandit algorithms. We also show that this advantage comes with a trade-off: the hypothesis space of the soft tree ensemble model is more constrained than that of a ReLU-based neural network. | https://openreview.net/pdf/5f22cbcc4e1f4f15297ff48ae857d328731de108.pdf |
Transfer Learning for Diffusion Models | https://openreview.net/forum?id=6emETARnWi | https://openreview.net/forum?id=6emETARnWi | Yidong Ouyang,Liyan Xie,Hongyuan Zha,Guang Cheng | NIPS 2024,Poster | Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical prerequisite for their notable success lies in the presence of a substantial number of training samples, which can be impractical in real-world applications due to high collection costs or associated risks. Consequently, various finetuning and regularization approaches have been proposed to transfer knowledge from existing pre-trained models to specific target domains with limited data. This paper introduces the Transfer Guided Diffusion Process (TGDP), a novel approach distinct from conventional finetuning and regularization methods.
We prove that the optimal diffusion model for the target domain integrates pre-trained diffusion models on the source domain with additional guidance from a domain classifier.
We further extend TGDP to a conditional version for modeling the joint distribution of data and its corresponding labels, together with two additional regularization terms to enhance the model performance. We validate the effectiveness of TGDP on both simulated and real-world datasets. | https://openreview.net/pdf/b69df4de4e1f7e40fdc5f172023f56acde8ecf7a.pdf |
Clustering in Causal Attention Masking | https://openreview.net/forum?id=OiVxYf9trg | https://openreview.net/forum?id=OiVxYf9trg | Nikita Karagodin,Yury Polyanskiy,Philippe Rigollet | NIPS 2024,Poster | This work presents a modification of the self-attention dynamics proposed in Geshkovski et al to better reflect the practically relevant, causally masked attention used in transformer architectures for generative AI. This modification translates into an interacting particle system that cannot be interpreted as a mean-field gradient flow. Despite this loss of structure, we significantly strengthen the results of Geshkovski et al in this context: While previous rigorous results focused on cases where all three matrices (key, query, and value) were scaled identities, we prove asymptotic convergence to a single cluster for arbitrary key-query matrices and value matrix equal to the identity.
Additionally, we establish a connection to the classical R\'enyi parking problem from combinatorial geometry to make initial theoretical steps towards demonstrating the existence of meta-stable states. | https://openreview.net/pdf/1361fab0c43791b9c9dcfb0a70e718f4ecb7d356.pdf |
Active Set Ordering | https://openreview.net/forum?id=GkJbXpd3wM | https://openreview.net/forum?id=GkJbXpd3wM | Quoc Phong Nguyen,Sunil Gupta,Svetha Venkatesh,Bryan Kian Hsiang Low,Patrick Jaillet | NIPS 2024,Poster | In this paper, we formalize the active set ordering problem, which involves actively discovering a set of inputs based on their orderings determined by expensive evaluations of a blackbox function. We then propose the mean prediction (MP) algorithm and theoretically analyze it in terms of the regret of predicted pairwise orderings between inputs. Notably, as a special case of this framework, we can cast Bayesian optimization as an active set ordering problem by recognizing that maximizers can be identified solely by comparison rather than by precisely estimating the function evaluations. As a result, we are able to construct the popular Gaussian process upper confidence bound (GP-UCB) algorithm through the lens of ordering with several nuanced insights. We empirically validate the performance of our proposed solution using various synthetic functions and real-world datasets. | https://openreview.net/pdf/a84bb38a2dbbe31a1fdccd16481727e5c72a82a0.pdf |
HGDL: Heterogeneous Graph Label Distribution Learning | https://openreview.net/forum?id=OwguhIAh8R | https://openreview.net/forum?id=OwguhIAh8R | Yufei Jin,Heng Lian,Yi He,Xingquan Zhu | NIPS 2024,Poster | Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification.
This paper advances LDL into graph domains and aims to tackle a novel and fundamental
heterogeneous graph label distribution learning (HGDL) problem.
We argue that
the graph heterogeneity reflected on node types, node attributes, and neighborhood structures can
impose significant challenges for generalizing
LDL onto graphs.
To address the challenges, we propose a new
learning framework with two key components:
1) proactive graph topology homogenization,
and 2) topology and content consistency-aware graph transformer.
Specifically,
the former learns optimal information aggregation between meta-paths, so that the node
heterogeneity can be proactively addressed prior to the succeeding embedding learning; the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, \method~delivers
an end-to-end solution for learning and predicting label distribution for nodes.
Both theoretical and empirical studies substantiate
the effectiveness of our HGDL approach.
Our code and datasets are available at https://github.com/Listener-Watcher/HGDL. | https://openreview.net/pdf/c98fe7ec6e30f7f8475be8f25e8e979518ad86be.pdf |
Compressing Large Language Models using Low Rank and Low Precision Decomposition | https://openreview.net/forum?id=lkx3OpcqSZ | https://openreview.net/forum?id=lkx3OpcqSZ | Rajarshi Saha,Naomi Sagan,Varun Srivastava,Andrea Goldsmith,Mert Pilanci | NIPS 2024,Poster | The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $\rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inherent low-rank structure of a weight matrix $\mathbf{W}$ by approximating it via a low-rank, low-precision decomposition as $\mathbf{W} \approx \mathbf{Q} + \mathbf{L}\mathbf{R}$. Here, $\mathbf{L}$ and $\mathbf{R}$ are low rank factors, and the entries of $\mathbf{Q}$, $\mathbf{L}$ and $\mathbf{R}$ are quantized. The model is compressed by substituting each layer with its $\mathbf{Q} + \mathbf{L}\mathbf{R}$ decomposition, and the zero-shot performance of the compressed model is evaluated. Additionally, $\mathbf{L}$ and $\mathbf{R}$ are readily amenable to low-rank adaptation, consequently enhancing the zero-shot performance. $\rm CALDERA$ obtains this decomposition by formulating it as an optimization problem $\min_{\mathbf{Q},\mathbf{L},\mathbf{R}}\lVert(\mathbf{Q} + \mathbf{L}\mathbf{R} - \mathbf{W})\mathbf{X}^\top\rVert_{\rm F}^2$, where $\mathbf{X}$ is the calibration data, and $\mathbf{Q}, \mathbf{L}, \mathbf{R}$ are constrained to be representable using low-precision formats. Theoretical upper bounds on the approximation error of $\rm CALDERA$ are established using a rank-constrained regression framework, and the tradeoff between compression ratio and model performance is studied by analyzing the impact of target rank and quantization bit budget. Results illustrate that compressing LlaMa-$2$ $7$B/$13$B/$70$B and LlaMa-$3$ $8$B models obtained using $\rm CALDERA$ outperforms existing post-training LLM compression techniques in the regime of less than $2.5$ bits per parameter. | https://openreview.net/pdf/2b6005c971c3343b98f66b536c29add85a496414.pdf |
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning | https://openreview.net/forum?id=aDQlAz09dS | https://openreview.net/forum?id=aDQlAz09dS | Xuechen Zhang,Zijian Huang,Ege Onur Taga,Carlee Joe-Wong,Samet Oymak,Jiasi Chen | NIPS 2024,Poster | Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question (i.e., the specific prompt). At the same time, users often have a limit on monetary budget and latency to answer all their questions, and they do not know which LLMs to choose for each question to meet their accuracy and long term budget requirements. To navigate this rich design space, we propose TREACLE (Thrifty Reasoning via Context-Aware LLM and Prompt Selection), a reinforcement learning policy that jointly selects the model and prompting scheme while respecting the user's monetary cost and latency constraints. TREACLE uses the problem context, including question text embeddings (reflecting the type or difficulty of a query) and the response history (reflecting the consistency of previous responses) to make smart decisions. Our evaluations on standard reasoning datasets (GSM8K, CSQA, and LLC) with various LLMs and prompts show that TREACLE enables cost savings of up to 85% compared to baselines, while maintaining high accuracy. Importantly, it provides the user with the ability to gracefully trade off accuracy for cost. | https://openreview.net/pdf/6edc8a474ffa7f439968f38dd2ced40f203ae8db.pdf |
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